From 2162f732da5e218e85449fd44664cc49723466a0 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 12 Mar 2025 16:25:31 +0100
Subject: [PATCH 01/60] add simple did multi simulation

---
 results/did/did_pa_multi_coverage.csv         |   5 +
 .../did/did_pa_multi_coverage_metadata.csv    |   2 +
 scripts/did/did_pa_multi_coverage.py          | 165 ++++++++++++++++++
 3 files changed, 172 insertions(+)
 create mode 100644 results/did/did_pa_multi_coverage.csv
 create mode 100644 results/did/did_pa_multi_coverage_metadata.csv
 create mode 100644 scripts/did/did_pa_multi_coverage.py

diff --git a/results/did/did_pa_multi_coverage.csv b/results/did/did_pa_multi_coverage.csv
new file mode 100644
index 0000000..6e5e19f
--- /dev/null
+++ b/results/did/did_pa_multi_coverage.csv
@@ -0,0 +1,5 @@
+Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
+Linear,Linear,observational,False,1,0.9,0.9029166666666666,0.3186627096956461,0.07711696371410508,0.901,0.49537746791429166,1000
+Linear,Linear,observational,False,1,0.95,0.95125,0.3797100386232941,0.07711696371410508,0.95,0.5448416509948917,1000
+Linear,Linear,observational,True,1,0.9,0.9003333333333333,0.3167085369567106,0.07700388113671185,0.901,0.49287577992610865,1000
+Linear,Linear,observational,True,1,0.95,0.9498333333333334,0.37738149818350913,0.07700388113671185,0.948,0.541465330653648,1000
diff --git a/results/did/did_pa_multi_coverage_metadata.csv b/results/did/did_pa_multi_coverage_metadata.csv
new file mode 100644
index 0000000..5398bd1
--- /dev/null
+++ b/results/did/did_pa_multi_coverage_metadata.csv
@@ -0,0 +1,2 @@
+DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
+0.10.dev0,did_pa_multi_coverage.py,2025-03-12 16:23:08,1368.6782059669495,3.11.9
diff --git a/scripts/did/did_pa_multi_coverage.py b/scripts/did/did_pa_multi_coverage.py
new file mode 100644
index 0000000..c983f4e
--- /dev/null
+++ b/scripts/did/did_pa_multi_coverage.py
@@ -0,0 +1,165 @@
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import time
+import sys
+
+from sklearn.linear_model import LinearRegression, LogisticRegression
+
+import doubleml as dml
+from doubleml.did.datasets import make_did_CS2021
+
+# Number of repetitions
+n_rep = 1000
+max_runtime = 5.5 * 3600  # 5.5 hours in seconds
+
+# DGP pars
+dgp_dict = {}
+
+df_oracle = make_did_CS2021(n_obs=int(1e+6), dgp_type=1)
+df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"]
+df_oracle_thetas = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
+# drop 
+print(df_oracle_thetas)
+
+n_obs = 2000
+
+# to get the best possible comparison between different learners (and settings) we first simulate all datasets
+np.random.seed(42)
+
+dgp_types = [1]
+n_dgps = len(dgp_types)
+datasets = []
+for dgp_type in dgp_types:
+    datasets_dgp = []
+    for i in range(n_rep):
+        data = make_did_CS2021(n_obs=n_obs, dgp_type=dgp_type)
+        datasets_dgp.append(data)
+    datasets.append(datasets_dgp)
+
+
+# set up hyperparameters
+hyperparam_dict = {
+    "DGP": dgp_types,
+    "score": ["observational"],
+    "in sample normalization": [True, False],
+    "learner_g": [("Linear", LinearRegression()),],
+    "learner_m": [("Linear", LogisticRegression()),],
+    "level": [0.95, 0.90]
+}
+
+# set up the results dataframe
+df_results_detailed = pd.DataFrame()
+
+# start simulation
+np.random.seed(42)
+start_time = time.time()
+
+for i_rep in range(n_rep):
+    print(f"Repetition: {i_rep + 1}/{n_rep}", end="\r")
+
+    # Check the elapsed time
+    elapsed_time = time.time() - start_time
+    if elapsed_time > max_runtime:
+        print("Maximum runtime exceeded. Stopping the simulation.")
+        break
+
+    for i_dgp, dgp_type in enumerate(dgp_types):
+        # define the DoubleML data object
+        obj_dml_data = dml.data.DoubleMLPanelData(
+            datasets[i_dgp][i_rep],
+            y_col="y",
+            d_cols="d",
+            id_col="id",
+            t_col="t",
+            x_cols=["Z1", "Z2", "Z3", "Z4"],
+        )
+
+        for learner_g_idx, (learner_g_name, ml_g) in enumerate(hyperparam_dict["learner_g"]):
+            for learner_m_idx, (learner_m_name, ml_m) in enumerate(hyperparam_dict["learner_m"]):
+                for score in hyperparam_dict["score"]:
+                    for in_sample_normalization in hyperparam_dict["in sample normalization"]:
+                        if score == "experimental":
+                            dml_DiD = dml.did.DoubleMLDIDMulti(
+                                obj_dml_data=obj_dml_data,
+                                ml_g=ml_g,
+                                ml_m=None,
+                                gt_combinations="standard",
+                                score=score,
+                                in_sample_normalization=in_sample_normalization)
+                        else:
+                            assert score == "observational"
+                            dml_DiD = dml.did.DoubleMLDIDMulti(
+                                obj_dml_data=obj_dml_data,
+                                ml_g=ml_g,
+                                ml_m=ml_m,
+                                gt_combinations="standard",
+                                score=score,
+                                in_sample_normalization=in_sample_normalization)
+                        dml_DiD.fit(n_jobs_cv=5)
+
+                        # oracle values
+                        theta = np.full_like(dml_DiD.coef, np.nan)
+                        for i, (g, _, t) in enumerate(dml_DiD.gt_combinations):
+                            group_index = df_oracle_thetas["d"] == g 
+                            time_index = df_oracle_thetas["t"] == t
+                            theta[i] = df_oracle_thetas[group_index & time_index]["ite"].iloc[0]
+
+                        for level_idx, level in enumerate(hyperparam_dict["level"]):
+                            confint = dml_DiD.confint(level=level)
+                            coverage = np.mean((confint.iloc[:, 0] < theta) & (theta < confint.iloc[:, 1]))
+                            ci_length = np.mean(confint.iloc[:, 1] - confint.iloc[:, 0])
+                            bias = np.mean(abs(dml_DiD.coef - theta))
+
+                            dml_DiD.bootstrap(n_rep_boot=2000)
+                            confint_uniform = dml_DiD.confint(level=level, joint=True)
+
+                            coverage_uniform = all((confint_uniform.iloc[:, 0] < theta) & (theta < confint_uniform.iloc[:, 1]))
+                            ci_length_uniform = np.mean(confint_uniform.iloc[:, 1] - confint_uniform.iloc[:, 0])
+
+                            df_results_detailed = pd.concat(
+                                (df_results_detailed,
+                                 pd.DataFrame({
+                                    "Coverage": coverage,
+                                    "CI Length": ci_length,
+                                    "Bias": bias,
+                                    "Uniform Coverage": coverage_uniform,
+                                    "Uniform CI Length": ci_length_uniform,
+                                    "Learner g": learner_g_name,
+                                    "Learner m": learner_m_name,
+                                    "Score": score,
+                                    "In-sample-norm.": in_sample_normalization,
+                                    "DGP": dgp_type,
+                                    "level": level,
+                                    "repetition": i_rep}, index=[0])),
+                                ignore_index=True)
+
+df_results = df_results_detailed.groupby(
+    ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]).agg(
+        {"Coverage": "mean",
+         "CI Length": "mean",
+         "Bias": "mean",
+         "Uniform Coverage": "mean",
+         "Uniform CI Length": "mean",
+         "repetition": "count"}
+    ).reset_index()
+print(df_results)
+
+end_time = time.time()
+total_runtime = end_time - start_time
+
+# save results
+script_name = "did_pa_multi_coverage.py"
+path = "results/did/did_pa_multi_coverage"
+
+metadata = pd.DataFrame({
+    'DoubleML Version': [dml.__version__],
+    'Script': [script_name],
+    'Date': [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
+    'Total Runtime (seconds)': [total_runtime],
+    'Python Version': [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"],
+})
+print(metadata)
+
+df_results.to_csv(f"{path}.csv", index=False)
+metadata.to_csv(f"{path}_metadata.csv", index=False)

From 6557a2ff776011d0bd372fea349fa1e0e2a49658 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 12 Mar 2025 16:28:50 +0100
Subject: [PATCH 02/60] add to coverage website

---
 doc/_website.yml      |   1 +
 doc/did/did_multi.qmd | 108 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 109 insertions(+)
 create mode 100644 doc/did/did_multi.qmd

diff --git a/doc/_website.yml b/doc/_website.yml
index 6dd3260..4beb151 100644
--- a/doc/_website.yml
+++ b/doc/_website.yml
@@ -25,6 +25,7 @@ website:
           - plm/pliv.qmd
       - text: "DID"
         menu:
+          - did/did_multi.qmd
           - did/did_pa.qmd
           - did/did_cs.qmd
       - text: "SSM"
diff --git a/doc/did/did_multi.qmd b/doc/did/did_multi.qmd
new file mode 100644
index 0000000..e2fc2ae
--- /dev/null
+++ b/doc/did/did_multi.qmd
@@ -0,0 +1,108 @@
+---
+title: "DiD for Panel Data over Multiple Periods"
+
+jupyter: python3
+---
+
+```{python}
+#| echo: false
+
+import numpy as np
+import pandas as pd
+from itables import init_notebook_mode, show, options
+
+init_notebook_mode(all_interactive=True)
+
+def highlight_range(s, level=0.95, dist=0.05, props=''):
+    color_grid = np.where((s >= level-dist) &
+                          (s <= level+dist), props, '')
+    return color_grid
+
+
+def color_coverage(df, level):
+    # color coverage column order is important
+    styled_df = df.apply(
+        highlight_range,
+        level=level,
+        dist=1.0,
+        props='color:black;background-color:red',
+        subset=["Coverage", "Uniform Coverage"])
+    styled_df = styled_df.apply(
+        highlight_range,
+        level=level,
+        dist=0.1,
+        props='color:black;background-color:yellow',
+        subset=["Coverage", "Uniform Coverage"])
+    styled_df = styled_df.apply(
+        highlight_range,
+        level=level,
+        dist=0.05,
+        props='color:white;background-color:darkgreen',
+        subset=["Coverage", "Uniform Coverage"])
+
+    # set all coverage values to bold
+    styled_df = styled_df.set_properties(
+        **{'font-weight': 'bold'},
+        subset=["Coverage", "Uniform Coverage"])
+    return styled_df
+
+
+def make_pretty(df, level, n_rep):
+    styled_df = df.style.hide(axis="index")
+    # Format only float columns
+    float_cols = df.select_dtypes(include=['float']).columns
+    styled_df = styled_df.format({col: "{:.3f}" for col in float_cols})
+
+    # color coverage column order is important
+    styled_df = color_coverage(styled_df, level)
+    caption = f"Coverage for {level*100}%-Confidence Interval over {n_rep} Repetitions"
+
+    return show(styled_df, caption=caption)
+```
+
+## ATTE Coverage
+
+The simulations are based on the  the [make_did_SZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_did_SZ2020.html)-DGP with $1000$ observations. Learners are only set to boosting, due to time constraints (and the nonlinearity of some of the DGPs).
+
+::: {.callout-note title="Metadata"  collapse="true"}
+
+```{python}
+#| echo: false
+metadata_file = '../../results/did/did_pa_multi_coverage_metadata.csv'
+metadata_df = pd.read_csv(metadata_file)
+print(metadata_df.T.to_string(header=False))
+```
+
+:::
+
+```{python}
+#| echo: false
+
+# set up data
+df = pd.read_csv("../../results/did/did_pa_multi_coverage.csv", index_col=None)
+
+assert df["repetition"].nunique() == 1
+n_rep = df["repetition"].unique()[0]
+
+display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"]
+```
+
+### Observational Score 
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```

From 2184a709409f1a5e96dd66d56d2ed53936bcdbc2 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 17 Mar 2025 07:20:50 +0100
Subject: [PATCH 03/60] add option for None in did multi (ml_m)

---
 scripts/did/did_pa_multi_coverage.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/scripts/did/did_pa_multi_coverage.py b/scripts/did/did_pa_multi_coverage.py
index c983f4e..667fafd 100644
--- a/scripts/did/did_pa_multi_coverage.py
+++ b/scripts/did/did_pa_multi_coverage.py
@@ -5,6 +5,7 @@
 import sys
 
 from sklearn.linear_model import LinearRegression, LogisticRegression
+from lightgbm import LGBMRegressor, LGBMClassifier
 
 import doubleml as dml
 from doubleml.did.datasets import make_did_CS2021
@@ -16,10 +17,10 @@
 # DGP pars
 dgp_dict = {}
 
-df_oracle = make_did_CS2021(n_obs=int(1e+6), dgp_type=1)
+df_oracle = make_did_CS2021(n_obs=int(1e+6), dgp_type=1)  # does not depend on the DGP type
 df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"]
 df_oracle_thetas = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
-# drop 
+# drop
 print(df_oracle_thetas)
 
 n_obs = 2000
@@ -27,7 +28,7 @@
 # to get the best possible comparison between different learners (and settings) we first simulate all datasets
 np.random.seed(42)
 
-dgp_types = [1]
+dgp_types = [1, 2, 3, 4, 5, 6]
 n_dgps = len(dgp_types)
 datasets = []
 for dgp_type in dgp_types:
@@ -41,10 +42,12 @@
 # set up hyperparameters
 hyperparam_dict = {
     "DGP": dgp_types,
-    "score": ["observational"],
+    "score": ["observational", "experimental"],
     "in sample normalization": [True, False],
-    "learner_g": [("Linear", LinearRegression()),],
-    "learner_m": [("Linear", LogisticRegression()),],
+    "learner_g": [("Linear", LinearRegression()),
+                  ("LGBM", LGBMRegressor(n_estimators=300, learning_rate=0.05, verbose=-1)),],
+    "learner_m": [("Linear", LogisticRegression()),
+                  ("LGBM", LGBMClassifier(n_estimators=300, learning_rate=0.05, verbose=-1))],
     "level": [0.95, 0.90]
 }
 

From df7222da860c63cba3be4ca2bc0189135c70aa69 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 07:52:28 +0100
Subject: [PATCH 04/60] updated did sim

---
 results/did/did_pa_multi_coverage.csv         | 28 +++++-
 .../did/did_pa_multi_coverage_metadata.csv    |  2 +-
 scripts/did/did_pa_multi_agg_coverage.py      |  0
 scripts/did/did_pa_multi_coverage.py          | 96 ++++++++++++++-----
 4 files changed, 98 insertions(+), 28 deletions(-)
 create mode 100644 scripts/did/did_pa_multi_agg_coverage.py

diff --git a/results/did/did_pa_multi_coverage.csv b/results/did/did_pa_multi_coverage.csv
index 6e5e19f..8276aac 100644
--- a/results/did/did_pa_multi_coverage.csv
+++ b/results/did/did_pa_multi_coverage.csv
@@ -1,5 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,observational,False,1,0.9,0.9029166666666666,0.3186627096956461,0.07711696371410508,0.901,0.49537746791429166,1000
-Linear,Linear,observational,False,1,0.95,0.95125,0.3797100386232941,0.07711696371410508,0.95,0.5448416509948917,1000
-Linear,Linear,observational,True,1,0.9,0.9003333333333333,0.3167085369567106,0.07700388113671185,0.901,0.49287577992610865,1000
-Linear,Linear,observational,True,1,0.95,0.9498333333333334,0.37738149818350913,0.07700388113671185,0.948,0.541465330653648,1000
+Linear,Linear,experimental,False,1,0.9,0.8875,0.589658704307476,0.14271881238392356,0.85,0.9184713776325004,20
+Linear,Linear,experimental,False,1,0.95,0.9458333333333334,0.7026216829731943,0.14271881238392356,0.9,1.0088572704968817,20
+Linear,Linear,experimental,False,4,0.9,0.7041666666666667,2.0297642964886093,0.9047675553035617,0.55,2.9260160123442596,20
+Linear,Linear,experimental,False,4,0.95,0.7708333333333333,2.418613336188561,0.9047675553035617,0.65,3.265541044699371,20
+Linear,Linear,experimental,False,6,0.9,0.9375,1.9858835644872976,0.4370880265956636,0.9,2.8747704680291024,20
+Linear,Linear,experimental,False,6,0.95,0.975,2.3663262190076697,0.4370880265956636,0.9,3.2067165503022985,20
+Linear,Linear,experimental,True,1,0.9,0.9041666666666666,0.5893929319394263,0.1419165948760371,0.9,0.9179189354368003,20
+Linear,Linear,experimental,True,1,0.95,0.9458333333333334,0.7023049956638021,0.1419165948760371,0.9,1.0086412447991253,20
+Linear,Linear,experimental,True,4,0.9,0.7041666666666667,2.031873164323966,0.9059623323417508,0.6,2.943810800692373,20
+Linear,Linear,experimental,True,4,0.95,0.7833333333333333,2.4211262072050013,0.9059623323417508,0.6,3.2739360531515684,20
+Linear,Linear,experimental,True,6,0.9,0.9458333333333334,1.9877755956053036,0.4386853538456557,0.9,2.8801526124840118,20
+Linear,Linear,experimental,True,6,0.95,0.975,2.3685807131390373,0.4386853538456557,0.9,3.2111476829248415,20
+Linear,Linear,observational,False,1,0.9,0.9125,0.6827078489949769,0.15467502808332462,0.9,1.0590069349882765,20
+Linear,Linear,observational,False,1,0.95,0.9583333333333334,0.8134965774875249,0.15467502808332462,1.0,1.1738447754301748,20
+Linear,Linear,observational,False,4,0.9,0.8,2.7854153587870214,0.8355183226583197,0.7,4.0015833335633415,20
+Linear,Linear,observational,False,4,0.95,0.8416666666666666,3.3190271132668636,0.8355183226583197,0.8,4.483472762454467,20
+Linear,Linear,observational,False,6,0.9,0.9333333333333333,2.5337718344427866,0.5967437923241619,0.9,3.6412755250672886,20
+Linear,Linear,observational,False,6,0.95,0.9708333333333334,3.0191753595448407,0.5967437923241619,0.95,4.081839476947988,20
+Linear,Linear,observational,True,1,0.9,0.9041666666666666,0.6620805479575324,0.15571526121686724,0.85,1.0317138682400495,20
+Linear,Linear,observational,True,1,0.95,0.9541666666666666,0.7889176323040639,0.15571526121686724,0.95,1.1316182486711308,20
+Linear,Linear,observational,True,4,0.9,0.7916666666666667,2.631108576842307,0.8079463860509168,0.7,3.77565721661986,20
+Linear,Linear,observational,True,4,0.95,0.8416666666666666,3.1351592418487586,0.8079463860509168,0.75,4.24016752287414,20
+Linear,Linear,observational,True,6,0.9,0.9333333333333333,2.3368968698600474,0.5421819294976428,0.9,3.361076606239287,20
+Linear,Linear,observational,True,6,0.95,0.9791666666666666,2.784584369977626,0.5421819294976428,0.95,3.7810902213750666,20
diff --git a/results/did/did_pa_multi_coverage_metadata.csv b/results/did/did_pa_multi_coverage_metadata.csv
index 5398bd1..0cb88dc 100644
--- a/results/did/did_pa_multi_coverage_metadata.csv
+++ b/results/did/did_pa_multi_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,did_pa_multi_coverage.py,2025-03-12 16:23:08,1368.6782059669495,3.11.9
+0.10.dev0,did_pa_multi_coverage.py,2025-03-18 07:37:01,120.99546647071838,3.11.9
diff --git a/scripts/did/did_pa_multi_agg_coverage.py b/scripts/did/did_pa_multi_agg_coverage.py
new file mode 100644
index 0000000..e69de29
diff --git a/scripts/did/did_pa_multi_coverage.py b/scripts/did/did_pa_multi_coverage.py
index 667fafd..664bbf1 100644
--- a/scripts/did/did_pa_multi_coverage.py
+++ b/scripts/did/did_pa_multi_coverage.py
@@ -3,6 +3,7 @@
 from datetime import datetime
 import time
 import sys
+import warnings
 
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from lightgbm import LGBMRegressor, LGBMClassifier
@@ -10,49 +11,55 @@
 import doubleml as dml
 from doubleml.did.datasets import make_did_CS2021
 
+# Suppress warnings
+warnings.simplefilter(action='ignore', category=UserWarning)
+
 # Number of repetitions
-n_rep = 1000
+n_rep = 20
 max_runtime = 5.5 * 3600  # 5.5 hours in seconds
 
-# DGP pars
-dgp_dict = {}
-
+# Oracle values
 df_oracle = make_did_CS2021(n_obs=int(1e+6), dgp_type=1)  # does not depend on the DGP type
 df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"]
 df_oracle_thetas = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
-# drop
+print("ATTs:")
 print(df_oracle_thetas)
 
-n_obs = 2000
+df_oracle_post_treatment = df_oracle[df_oracle["t"] >= df_oracle["d"]]
+# Group aggregation
+df_oracle_agg_group = df_oracle_post_treatment.groupby("d")["ite"].mean()
+print("Group aggregated ATTs:")
+print(df_oracle_agg_group)
 
-# to get the best possible comparison between different learners (and settings) we first simulate all datasets
-np.random.seed(42)
+# Time aggregation
+df_oracle_agg_time = df_oracle_post_treatment.groupby("t")["ite"].mean()
+print("Time aggregated ATTs:")
+print(df_oracle_agg_time)
 
-dgp_types = [1, 2, 3, 4, 5, 6]
-n_dgps = len(dgp_types)
-datasets = []
-for dgp_type in dgp_types:
-    datasets_dgp = []
-    for i in range(n_rep):
-        data = make_did_CS2021(n_obs=n_obs, dgp_type=dgp_type)
-        datasets_dgp.append(data)
-    datasets.append(datasets_dgp)
+# Eventstudy aggregation
+df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype("datetime64[M]") - \
+    pd.to_datetime(df_oracle["d"]).values.astype("datetime64[M]")
+df_oracle_agg_eventstudy = df_oracle.groupby("e")["ite"].mean()
+print("Event Study aggregated ATTs:")
+print(df_oracle_agg_eventstudy)
 
+# DGP pars
+n_obs = 500
+dgp_types = [1, 4, 6]
 
 # set up hyperparameters
 hyperparam_dict = {
     "DGP": dgp_types,
     "score": ["observational", "experimental"],
     "in sample normalization": [True, False],
-    "learner_g": [("Linear", LinearRegression()),
-                  ("LGBM", LGBMRegressor(n_estimators=300, learning_rate=0.05, verbose=-1)),],
-    "learner_m": [("Linear", LogisticRegression()),
-                  ("LGBM", LGBMClassifier(n_estimators=300, learning_rate=0.05, verbose=-1))],
+    "learner_g": [("Linear", LinearRegression()),],
+    "learner_m": [("Linear", LogisticRegression()),],
     "level": [0.95, 0.90]
 }
 
 # set up the results dataframe
 df_results_detailed = pd.DataFrame()
+df_results_agg = pd.DataFrame()
 
 # start simulation
 np.random.seed(42)
@@ -69,8 +76,9 @@
 
     for i_dgp, dgp_type in enumerate(dgp_types):
         # define the DoubleML data object
+        data = make_did_CS2021(n_obs=n_obs, dgp_type=dgp_type)
         obj_dml_data = dml.data.DoubleMLPanelData(
-            datasets[i_dgp][i_rep],
+            data,
             y_col="y",
             d_cols="d",
             id_col="id",
@@ -104,7 +112,7 @@
                         # oracle values
                         theta = np.full_like(dml_DiD.coef, np.nan)
                         for i, (g, _, t) in enumerate(dml_DiD.gt_combinations):
-                            group_index = df_oracle_thetas["d"] == g 
+                            group_index = df_oracle_thetas["d"] == g
                             time_index = df_oracle_thetas["t"] == t
                             theta[i] = df_oracle_thetas[group_index & time_index]["ite"].iloc[0]
 
@@ -136,6 +144,37 @@
                                     "level": level,
                                     "repetition": i_rep}, index=[0])),
                                 ignore_index=True)
+                            
+                            # group aggregation
+                            group_agg = dml_DiD.aggregate(aggregation="group")
+                            group_confint = group_agg.aggregated_frameworks.confint(level=level)
+                            group_coverage = np.mean((group_confint.iloc[:, 0] < df_oracle_agg_group.values) & (df_oracle_agg_group.values < group_confint.iloc[:, 1]))
+                            group_ci_length = np.mean(group_confint.iloc[:, 1] - group_confint.iloc[:, 0])
+                            group_bias = np.mean(abs(group_agg.aggregated_frameworks.thetas - df_oracle_agg_group.values))
+
+                            group_agg.aggregated_frameworks.bootstrap(n_rep_boot=2000)
+                            group_confint_uniform = group_agg.aggregated_frameworks.confint(level=level, joint=True)
+                            group_coverage_uniform = all((group_confint_uniform.iloc[:, 0] < df_oracle_agg_group.values) & (df_oracle_agg_group.values < group_confint_uniform.iloc[:, 1]))
+                            group_ci_length_uniform = np.mean(group_confint_uniform.iloc[:, 1] - group_confint_uniform.iloc[:, 0])
+
+                            df_results_agg = pd.concat(
+                                (df_results_agg,
+                                 pd.DataFrame({
+                                    "Coverage": group_coverage,
+                                    "CI Length": group_ci_length,
+                                    "Bias": group_bias,
+                                    "Uniform Coverage": group_coverage_uniform,
+                                    "Uniform CI Length": group_ci_length_uniform,
+                                    "Learner g": learner_g_name,
+                                    "Learner m": learner_m_name,
+                                    "Score": score,
+                                    "In-sample-norm.": in_sample_normalization,
+                                    "DGP": dgp_type,
+                                    "level": level,
+                                    "repetition": i_rep}, index=[0]),
+                                ),
+                                ignore_index=True)
+
 
 df_results = df_results_detailed.groupby(
     ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]).agg(
@@ -148,6 +187,17 @@
     ).reset_index()
 print(df_results)
 
+df_results_agg = df_results_agg.groupby(
+    ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]).agg(
+        {"Coverage": "mean",
+         "CI Length": "mean",
+         "Bias": "mean",
+         "Uniform Coverage": "mean",
+         "Uniform CI Length": "mean",
+         "repetition": "count"}
+    ).reset_index()
+print(df_results_agg)
+
 end_time = time.time()
 total_runtime = end_time - start_time
 

From 694d2e8c3ee6f2a5bea420412908cffe1596df3b Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 09:51:24 +0100
Subject: [PATCH 05/60] add a basic simulation package

---
 monte-cover/.python-version                 |    1 +
 monte-cover/README.md                       |    0
 monte-cover/pyproject.toml                  |   48 +
 monte-cover/src/montecover/__init__.py      |   13 +
 monte-cover/src/montecover/base.py          |   78 ++
 monte-cover/src/montecover/did/__init__.py  |    7 +
 monte-cover/src/montecover/did/did_multi.py |  231 ++++
 monte-cover/uv.lock                         | 1112 +++++++++++++++++++
 8 files changed, 1490 insertions(+)
 create mode 100644 monte-cover/.python-version
 create mode 100644 monte-cover/README.md
 create mode 100644 monte-cover/pyproject.toml
 create mode 100644 monte-cover/src/montecover/__init__.py
 create mode 100644 monte-cover/src/montecover/base.py
 create mode 100644 monte-cover/src/montecover/did/__init__.py
 create mode 100644 monte-cover/src/montecover/did/did_multi.py
 create mode 100644 monte-cover/uv.lock

diff --git a/monte-cover/.python-version b/monte-cover/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/monte-cover/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/monte-cover/README.md b/monte-cover/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/monte-cover/pyproject.toml b/monte-cover/pyproject.toml
new file mode 100644
index 0000000..6aee923
--- /dev/null
+++ b/monte-cover/pyproject.toml
@@ -0,0 +1,48 @@
+[project]
+name = "montecover"
+version = "0.1.0"
+description = "A Monte Carlo coverage simulation package"
+readme = "README.md"
+authors = [
+    { name = "SvenKlaassen", email = "sven.klaassen@uni-hamburg.de" }
+]
+requires-python = ">=3.12"
+dependencies = [
+    "black>=25.1.0",
+    "doubleml[rdd]>=0.9.3",
+    "ipykernel>=6.29.5",
+    "itables>=2.2.5",
+    "joblib>=1.4.2",
+    "lightgbm>=4.6.0",
+    "numpy>=2.2.4",
+    "pandas>=2.2.3",
+    "ruff>=0.11.0",
+    "scikit-learn>=1.5.2",
+]
+
+[project.scripts]
+monte-cover = "montecover:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.black]
+line-length = 127
+target-version = ['py311', 'py312']
+preview = true
+
+[tool.ruff]
+# max line length for black
+line-length = 127
+target-version = "py312"
+
+
+[tool.ruff.lint]
+# all rules can be found here: https://beta.ruff.rs/docs/rules/
+select = ["E", "F", "W", "I"]
+ignore = [
+    # Use `is` and `is not` for type comparisons, or `isinstance()` for
+    # isinstance checks
+    "E721",
+]
diff --git a/monte-cover/src/montecover/__init__.py b/monte-cover/src/montecover/__init__.py
new file mode 100644
index 0000000..e8b95c2
--- /dev/null
+++ b/monte-cover/src/montecover/__init__.py
@@ -0,0 +1,13 @@
+"""Monte Carlo coverage simulation package for statistical methods."""
+
+from importlib.metadata import version
+
+__version__ = version("montecover")
+
+from .base import BaseSimulation
+
+__all__ = ["BaseSimulation"]
+
+
+def main() -> None:
+    print("Hello from monte-cover!")
diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
new file mode 100644
index 0000000..a07d2a1
--- /dev/null
+++ b/monte-cover/src/montecover/base.py
@@ -0,0 +1,78 @@
+import time
+import warnings
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List
+
+from itertools import product
+import numpy as np
+
+
+class BaseSimulation(ABC):
+    """Abstract base class for simulation studies."""
+
+    def __init__(
+        self,
+        n_rep: int = 20,
+        max_runtime: float = 5.5 * 3600,
+        random_seed: int = 42,
+        output_path: str = "results",
+        suppress_warnings: bool = True,
+    ):
+        self.n_rep = n_rep
+        self.max_runtime = max_runtime
+        self.random_seed = random_seed
+        self.output_path = output_path
+        self.suppress_warnings = suppress_warnings
+
+        # Results storage
+        self.results = []
+        self.aggregated_results = None
+
+        # Timing
+        self.start_time = None
+        self.end_time = None
+        self.total_runtime = None
+
+        # Set up environment
+        if suppress_warnings:
+            warnings.simplefilter(action="ignore", category=UserWarning)
+
+        np.random.seed(self.random_seed)
+
+    @abstractmethod
+    def setup_parameters(self) -> Dict[str, List[Any]]:
+        """Define simulation parameters."""
+        pass
+
+    @abstractmethod
+    def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]:
+        """Run a single repetition with the given parameters."""
+        pass
+
+    def run_simulation(self):
+        """Run the full simulation."""
+        self.start_time = time.time()
+        param_grid = self.setup_parameters()
+
+        # Loop through repetitions
+        for i_rep in range(self.n_rep):
+            print(f"Repetition: {i_rep + 1}/{self.n_rep}", end="\r")
+
+            # Check elapsed time
+            elapsed_time = time.time() - self.start_time
+            if elapsed_time > self.max_runtime:
+                print("Maximum runtime exceeded. Stopping the simulation.")
+                break
+
+            keys = param_grid.keys()
+            for values in product(*param_grid.values()):
+                params = dict(zip(keys, values))
+                result = self.run_single_rep(i_rep, params)
+                if result is not None:
+                    result["repetition"] = i_rep
+                    self.results.append(result)
+
+        self.end_time = time.time()
+        self.total_runtime = self.end_time - self.start_time
+
+    # Add other methods here...
diff --git a/monte-cover/src/montecover/did/__init__.py b/monte-cover/src/montecover/did/__init__.py
new file mode 100644
index 0000000..31d65c5
--- /dev/null
+++ b/monte-cover/src/montecover/did/__init__.py
@@ -0,0 +1,7 @@
+"""Monte Carlo coverage simulations for DiD."""
+
+
+from montecover.did.did_multi import DIDMultiCoverageSimulation
+
+
+__all__ = ["DIDMultiCoverageSimulation"]
\ No newline at end of file
diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
new file mode 100644
index 0000000..513073e
--- /dev/null
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -0,0 +1,231 @@
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import sys
+from typing import Dict, List, Any, Tuple
+
+from sklearn.linear_model import LinearRegression, LogisticRegression
+
+import doubleml as dml
+from doubleml.did.datasets import make_did_CS2021
+
+from montecover.base import BaseSimulation
+
+class DIDMultiCoverageSimulation(BaseSimulation):
+    """Simulation study for coverage properties of DoubleMLDIDMulti."""
+    
+    def __init__(
+        self,
+        n_rep: int = 20,
+        n_obs: int = 500,
+        max_runtime: float = 5.5 * 3600,
+        random_seed: int = 42,
+        output_path: str = "results/did/did_pa_multi_coverage",
+        suppress_warnings: bool = True,
+    ):
+        super().__init__(
+            n_rep=n_rep,
+            max_runtime=max_runtime,
+            random_seed=random_seed,
+            output_path=output_path,
+            suppress_warnings=suppress_warnings,
+        )
+        self.n_obs = n_obs
+        
+        # Calculate oracle values
+        self._calculate_oracle_values()
+        
+        # Additional results storage for aggregated results
+        self.results_aggregated = []
+    
+    def _calculate_oracle_values(self):
+        """Calculate oracle values for the simulation."""
+        # Oracle values
+        df_oracle = make_did_CS2021(n_obs=int(1e+6), dgp_type=1)  # does not depend on the DGP type
+        df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"]
+        self.oracle_thetas = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
+        print("ATTs:")
+        print(self.oracle_thetas)
+        
+        # Oracle group aggregation
+        df_oracle_post_treatment = df_oracle[df_oracle["t"] >= df_oracle["d"]]
+        self.oracle_agg_group = df_oracle_post_treatment.groupby("d")["ite"].mean()
+        print("Group aggregated ATTs:")
+        print(self.oracle_agg_group)
+        
+        # Oracle time aggregation
+        self.oracle_agg_time = df_oracle_post_treatment.groupby("t")["ite"].mean()
+        print("Time aggregated ATTs:")
+        print(self.oracle_agg_time)
+        
+        # Oracle eventstudy aggregation
+        df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype("datetime64[M]") - \
+            pd.to_datetime(df_oracle["d"]).values.astype("datetime64[M]")
+        self.oracle_agg_eventstudy = df_oracle.groupby("e")["ite"].mean()
+        print("Event Study aggregated ATTs:")
+        print(self.oracle_agg_eventstudy)
+    
+    def setup_parameters(self) -> Dict[str, List[Any]]:
+        """Define simulation parameters."""
+        return {
+            "DGP": [1, 4, 6],
+            "score": ["observational", "experimental"],
+            "in_sample_normalization": [True, False],
+            "learner_g": [("Linear", LinearRegression())],
+            "learner_m": [("Linear", LogisticRegression())],
+            "level": [0.95, 0.90]
+        }
+    
+    def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]:
+        """Run a single repetition with the given parameters."""
+        # Extract parameters
+        dgp_type = params["DGP"]
+        score = params["score"]
+        in_sample_normalization = params["in_sample_normalization"]
+        learner_g_name, ml_g = params["learner_g"]
+        learner_m_name, ml_m = params["learner_m"]
+        level = params["level"]
+        
+        # Generate data
+        data = make_did_CS2021(n_obs=self.n_obs, dgp_type=dgp_type)
+        obj_dml_data = dml.data.DoubleMLPanelData(
+            data,
+            y_col="y",
+            d_cols="d",
+            id_col="id",
+            t_col="t",
+            x_cols=["Z1", "Z2", "Z3", "Z4"],
+        )
+        
+        # Fit model
+        if score == "experimental":
+            dml_DiD = dml.did.DoubleMLDIDMulti(
+                obj_dml_data=obj_dml_data,
+                ml_g=ml_g,
+                ml_m=None,
+                gt_combinations="standard",
+                score=score,
+                in_sample_normalization=in_sample_normalization)
+        else:
+            dml_DiD = dml.did.DoubleMLDIDMulti(
+                obj_dml_data=obj_dml_data,
+                ml_g=ml_g,
+                ml_m=ml_m,
+                gt_combinations="standard",
+                score=score,
+                in_sample_normalization=in_sample_normalization)
+        
+        # Fit the model
+        dml_DiD.fit(n_jobs_cv=5)
+        
+        # Oracle values for this model
+        theta = np.full_like(dml_DiD.coef, np.nan)
+        for i, (g, _, t) in enumerate(dml_DiD.gt_combinations):
+            group_index = self.oracle_thetas["d"] == g
+            time_index = self.oracle_thetas["t"] == t
+            theta[i] = self.oracle_thetas[group_index & time_index]["ite"].iloc[0]
+        
+        # Confidence intervals and metrics
+        confint = dml_DiD.confint(level=level)
+        coverage = np.mean((confint.iloc[:, 0] < theta) & (theta < confint.iloc[:, 1]))
+        ci_length = np.mean(confint.iloc[:, 1] - confint.iloc[:, 0])
+        bias = np.mean(abs(dml_DiD.coef - theta))
+        
+        # Bootstrap for uniform confidence intervals
+        dml_DiD.bootstrap(n_rep_boot=2000)
+        confint_uniform = dml_DiD.confint(level=level, joint=True)
+        
+        coverage_uniform = all((confint_uniform.iloc[:, 0] < theta) & (theta < confint_uniform.iloc[:, 1]))
+        ci_length_uniform = np.mean(confint_uniform.iloc[:, 1] - confint_uniform.iloc[:, 0])
+        
+        # Detailed results
+        result_detailed = {
+            "Coverage": coverage,
+            "CI Length": ci_length,
+            "Bias": bias,
+            "Uniform Coverage": coverage_uniform,
+            "Uniform CI Length": ci_length_uniform,
+            "Learner g": learner_g_name,
+            "Learner m": learner_m_name,
+            "Score": score,
+            "In-sample-norm.": in_sample_normalization,
+            "DGP": dgp_type,
+            "level": level,
+        }
+        
+        # Group aggregation
+        group_agg = dml_DiD.aggregate(aggregation="group")
+        group_confint = group_agg.aggregated_frameworks.confint(level=level)
+        group_coverage = np.mean((group_confint.iloc[:, 0] < self.oracle_agg_group.values) & 
+                                (self.oracle_agg_group.values < group_confint.iloc[:, 1]))
+        group_ci_length = np.mean(group_confint.iloc[:, 1] - group_confint.iloc[:, 0])
+        group_bias = np.mean(abs(group_agg.aggregated_frameworks.thetas - self.oracle_agg_group.values))
+        
+        group_agg.aggregated_frameworks.bootstrap(n_rep_boot=2000)
+        group_confint_uniform = group_agg.aggregated_frameworks.confint(level=level, joint=True)
+        group_coverage_uniform = all((group_confint_uniform.iloc[:, 0] < self.oracle_agg_group.values) & 
+                                   (self.oracle_agg_group.values < group_confint_uniform.iloc[:, 1]))
+        group_ci_length_uniform = np.mean(group_confint_uniform.iloc[:, 1] - group_confint_uniform.iloc[:, 0])
+        
+        # Aggregated results
+        result_aggregated = {
+            "Coverage": group_coverage,
+            "CI Length": group_ci_length,
+            "Bias": group_bias,
+            "Uniform Coverage": group_coverage_uniform,
+            "Uniform CI Length": group_ci_length_uniform,
+            "Learner g": learner_g_name,
+            "Learner m": learner_m_name,
+            "Score": score,
+            "In-sample-norm.": in_sample_normalization,
+            "DGP": dgp_type,
+            "level": level,
+        }
+        
+        # Store aggregated results
+        self.results_aggregated.append(result_aggregated)
+        
+        return result_detailed
+    
+    def summarize_results(self):
+        """Summarize results and save to CSV."""
+        df_results_detailed = pd.DataFrame(self.results)
+        df_results_aggregated = pd.DataFrame(self.results_aggregated)
+        
+        # Aggregate results by parameters
+        df_summary_detailed = df_results_detailed.groupby(
+            ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]
+        ).agg({
+            "Coverage": "mean",
+            "CI Length": "mean",
+            "Bias": "mean",
+            "Uniform Coverage": "mean",
+            "Uniform CI Length": "mean",
+            "repetition": "count"
+        }).reset_index()
+        
+        df_summary_aggregated = df_results_aggregated.groupby(
+            ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]
+        ).agg({
+            "Coverage": "mean",
+            "CI Length": "mean",
+            "Bias": "mean",
+            "Uniform Coverage": "mean",
+            "Uniform CI Length": "mean",
+            "repetition": "count"
+        }).reset_index()
+        
+        # Save results
+        metadata = pd.DataFrame({
+            'DoubleML Version': [dml.__version__],
+            'Script': ['DIDMultiCoverageSimulation'],
+            'Date': [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
+            'Total Runtime (seconds)': [self.total_runtime],
+            'Python Version': [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"],
+        })
+        
+        df_summary_detailed.to_csv(f"{self.output_path}.csv", index=False)
+        df_summary_aggregated.to_csv(f"{self.output_path}_aggregated.csv", index=False)
+        metadata.to_csv(f"{self.output_path}_metadata.csv", index=False)
+        
+        return df_summary_detailed, df_summary_aggregated, metadata
\ No newline at end of file
diff --git a/monte-cover/uv.lock b/monte-cover/uv.lock
new file mode 100644
index 0000000..eed1b7c
--- /dev/null
+++ b/monte-cover/uv.lock
@@ -0,0 +1,1112 @@
+version = 1
+revision = 1
+requires-python = ">=3.12"
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/0f/dd/84f10e23edd882c6f968c21c2434fe67bd4a528967067515feca9e611e5e/tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639", size = 346762 },
+]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/63/53559446a878410fc5a5974feb13d31d78d752eb18aeba59c7fef1af7598/wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5", size = 101301 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166 },
+]

From b587615371ba2fd959e04d81834cdd737c2087d8 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 12:43:43 +0100
Subject: [PATCH 06/60] update sim classes

---
 monte-cover/src/montecover/__init__.py      |   2 +-
 monte-cover/src/montecover/base.py          |  63 ++++++--
 monte-cover/src/montecover/did/__init__.py  |   4 +-
 monte-cover/src/montecover/did/did_multi.py | 153 +++++++++-----------
 scripts/did/did_pa_multi_agg_coverage.py    |   9 ++
 5 files changed, 132 insertions(+), 99 deletions(-)

diff --git a/monte-cover/src/montecover/__init__.py b/monte-cover/src/montecover/__init__.py
index e8b95c2..8d60b6b 100644
--- a/monte-cover/src/montecover/__init__.py
+++ b/monte-cover/src/montecover/__init__.py
@@ -10,4 +10,4 @@
 
 
 def main() -> None:
-    print("Hello from monte-cover!")
+    print("Hello from monte-cover! Run your simulations here.")
diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index a07d2a1..dd2a7ee 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -1,10 +1,14 @@
 import time
 import warnings
+import pandas as pd
 from abc import ABC, abstractmethod
+from itertools import product
 from typing import Any, Dict, List
 
-from itertools import product
+import sys
+from datetime import datetime
 import numpy as np
+import doubleml as dml
 
 
 class BaseSimulation(ABC):
@@ -12,21 +16,21 @@ class BaseSimulation(ABC):
 
     def __init__(
         self,
-        n_rep: int = 20,
+        repetitions: int = 20,
         max_runtime: float = 5.5 * 3600,
         random_seed: int = 42,
         output_path: str = "results",
         suppress_warnings: bool = True,
     ):
-        self.n_rep = n_rep
+        self.repetitions = repetitions
         self.max_runtime = max_runtime
         self.random_seed = random_seed
         self.output_path = output_path
         self.suppress_warnings = suppress_warnings
 
         # Results storage
-        self.results = []
-        self.aggregated_results = None
+        self.results = dict()
+        self.result_summary = None
 
         # Timing
         self.start_time = None
@@ -49,14 +53,19 @@ def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]
         """Run a single repetition with the given parameters."""
         pass
 
+    @abstractmethod
+    def summarize_results(self) -> Dict[str, Any]:
+        """Summarize the simulation results."""
+        pass
+
     def run_simulation(self):
         """Run the full simulation."""
         self.start_time = time.time()
         param_grid = self.setup_parameters()
 
         # Loop through repetitions
-        for i_rep in range(self.n_rep):
-            print(f"Repetition: {i_rep + 1}/{self.n_rep}", end="\r")
+        for i_rep in range(self.repetitions):
+            print(f"Repetition: {i_rep + 1}/{self.repetitions}", end="\r")
 
             # Check elapsed time
             elapsed_time = time.time() - self.start_time
@@ -67,12 +76,42 @@ def run_simulation(self):
             keys = param_grid.keys()
             for values in product(*param_grid.values()):
                 params = dict(zip(keys, values))
-                result = self.run_single_rep(i_rep, params)
-                if result is not None:
-                    result["repetition"] = i_rep
-                    self.results.append(result)
+                repetition_results = self.run_single_rep(params)
+                if repetition_results is not None:
+                    assert isinstance(repetition_results, dict), "The result must be a dictionary."
+                    # Process each dataframe in the result dictionary
+                    for result_name, repetition_result in repetition_results.items():
+                        assert isinstance(repetition_result, dict), "Each result must be a dictionary."
+                        repetition_result["repetition"] = i_rep
+
+                        # Initialize key in results dict if not exists
+                        if result_name not in self.results:
+                            self.results[result_name] = []
+                        self.results[result_name].append(repetition_result)
+
+        # convert results to dataframes
+        for key, value in self.results.items():
+            self.results[key] = pd.DataFrame(value)
 
         self.end_time = time.time()
         self.total_runtime = self.end_time - self.start_time
 
-    # Add other methods here...
+        # Summarize & save results
+        self.result_summary = self.summarize_results()
+        print("Simulation finished.")
+
+    def save_results(self):
+        """Save the simulation results."""
+        metadata = pd.DataFrame(
+            {
+                "DoubleML Version": [dml.__version__],
+                "Script": ["DIDMultiCoverageSimulation"],
+                "Date": [datetime.now().strftime("%Y-%m-%d %H")],
+                "Total Runtime (seconds)": [self.total_runtime],
+                "Python Version": [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"],
+            }
+        )
+
+        for df_name, df in self.result_summary.items():
+            df.to_csv(f"{self.output_path}_{df_name}.csv", index=False)
+        metadata.to_csv(f"{self.output_path}_metadata.csv", index=False)
diff --git a/monte-cover/src/montecover/did/__init__.py b/monte-cover/src/montecover/did/__init__.py
index 31d65c5..24967e6 100644
--- a/monte-cover/src/montecover/did/__init__.py
+++ b/monte-cover/src/montecover/did/__init__.py
@@ -1,7 +1,5 @@
 """Monte Carlo coverage simulations for DiD."""
 
-
 from montecover.did.did_multi import DIDMultiCoverageSimulation
 
-
-__all__ = ["DIDMultiCoverageSimulation"]
\ No newline at end of file
+__all__ = ["DIDMultiCoverageSimulation"]
diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
index 513073e..f63f964 100644
--- a/monte-cover/src/montecover/did/did_multi.py
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -1,70 +1,69 @@
-import numpy as np
-import pandas as pd
-from datetime import datetime
-import sys
-from typing import Dict, List, Any, Tuple
-
-from sklearn.linear_model import LinearRegression, LogisticRegression
+from typing import Any, Dict, List
 
 import doubleml as dml
+import numpy as np
+import pandas as pd
 from doubleml.did.datasets import make_did_CS2021
+from sklearn.linear_model import LinearRegression, LogisticRegression
 
 from montecover.base import BaseSimulation
 
+
 class DIDMultiCoverageSimulation(BaseSimulation):
     """Simulation study for coverage properties of DoubleMLDIDMulti."""
-    
+
     def __init__(
         self,
-        n_rep: int = 20,
+        output_path: str,
+        repetitions: int = 20,
         n_obs: int = 500,
         max_runtime: float = 5.5 * 3600,
         random_seed: int = 42,
-        output_path: str = "results/did/did_pa_multi_coverage",
         suppress_warnings: bool = True,
     ):
         super().__init__(
-            n_rep=n_rep,
+            repetitions=repetitions,
             max_runtime=max_runtime,
             random_seed=random_seed,
             output_path=output_path,
             suppress_warnings=suppress_warnings,
         )
         self.n_obs = n_obs
-        
+
         # Calculate oracle values
         self._calculate_oracle_values()
-        
+
         # Additional results storage for aggregated results
         self.results_aggregated = []
-    
+
     def _calculate_oracle_values(self):
         """Calculate oracle values for the simulation."""
         # Oracle values
-        df_oracle = make_did_CS2021(n_obs=int(1e+6), dgp_type=1)  # does not depend on the DGP type
+        df_oracle = make_did_CS2021(n_obs=int(1e6), dgp_type=1)  # does not depend on the DGP type
         df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"]
         self.oracle_thetas = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
         print("ATTs:")
         print(self.oracle_thetas)
-        
+
         # Oracle group aggregation
         df_oracle_post_treatment = df_oracle[df_oracle["t"] >= df_oracle["d"]]
         self.oracle_agg_group = df_oracle_post_treatment.groupby("d")["ite"].mean()
         print("Group aggregated ATTs:")
         print(self.oracle_agg_group)
-        
+
         # Oracle time aggregation
         self.oracle_agg_time = df_oracle_post_treatment.groupby("t")["ite"].mean()
         print("Time aggregated ATTs:")
         print(self.oracle_agg_time)
-        
+
         # Oracle eventstudy aggregation
-        df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype("datetime64[M]") - \
-            pd.to_datetime(df_oracle["d"]).values.astype("datetime64[M]")
+        df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype("datetime64[M]") - pd.to_datetime(
+            df_oracle["d"]
+        ).values.astype("datetime64[M]")
         self.oracle_agg_eventstudy = df_oracle.groupby("e")["ite"].mean()
         print("Event Study aggregated ATTs:")
         print(self.oracle_agg_eventstudy)
-    
+
     def setup_parameters(self) -> Dict[str, List[Any]]:
         """Define simulation parameters."""
         return {
@@ -73,10 +72,10 @@ def setup_parameters(self) -> Dict[str, List[Any]]:
             "in_sample_normalization": [True, False],
             "learner_g": [("Linear", LinearRegression())],
             "learner_m": [("Linear", LogisticRegression())],
-            "level": [0.95, 0.90]
+            "level": [0.95, 0.90],
         }
-    
-    def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]:
+
+    def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
         """Run a single repetition with the given parameters."""
         # Extract parameters
         dgp_type = params["DGP"]
@@ -85,7 +84,7 @@ def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]
         learner_g_name, ml_g = params["learner_g"]
         learner_m_name, ml_m = params["learner_m"]
         level = params["level"]
-        
+
         # Generate data
         data = make_did_CS2021(n_obs=self.n_obs, dgp_type=dgp_type)
         obj_dml_data = dml.data.DoubleMLPanelData(
@@ -96,7 +95,7 @@ def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]
             t_col="t",
             x_cols=["Z1", "Z2", "Z3", "Z4"],
         )
-        
+
         # Fit model
         if score == "experimental":
             dml_DiD = dml.did.DoubleMLDIDMulti(
@@ -105,7 +104,8 @@ def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]
                 ml_m=None,
                 gt_combinations="standard",
                 score=score,
-                in_sample_normalization=in_sample_normalization)
+                in_sample_normalization=in_sample_normalization,
+            )
         else:
             dml_DiD = dml.did.DoubleMLDIDMulti(
                 obj_dml_data=obj_dml_data,
@@ -113,31 +113,32 @@ def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]
                 ml_m=ml_m,
                 gt_combinations="standard",
                 score=score,
-                in_sample_normalization=in_sample_normalization)
-        
+                in_sample_normalization=in_sample_normalization,
+            )
+
         # Fit the model
         dml_DiD.fit(n_jobs_cv=5)
-        
+
         # Oracle values for this model
         theta = np.full_like(dml_DiD.coef, np.nan)
         for i, (g, _, t) in enumerate(dml_DiD.gt_combinations):
             group_index = self.oracle_thetas["d"] == g
             time_index = self.oracle_thetas["t"] == t
             theta[i] = self.oracle_thetas[group_index & time_index]["ite"].iloc[0]
-        
+
         # Confidence intervals and metrics
         confint = dml_DiD.confint(level=level)
         coverage = np.mean((confint.iloc[:, 0] < theta) & (theta < confint.iloc[:, 1]))
         ci_length = np.mean(confint.iloc[:, 1] - confint.iloc[:, 0])
         bias = np.mean(abs(dml_DiD.coef - theta))
-        
+
         # Bootstrap for uniform confidence intervals
         dml_DiD.bootstrap(n_rep_boot=2000)
         confint_uniform = dml_DiD.confint(level=level, joint=True)
-        
+
         coverage_uniform = all((confint_uniform.iloc[:, 0] < theta) & (theta < confint_uniform.iloc[:, 1]))
         ci_length_uniform = np.mean(confint_uniform.iloc[:, 1] - confint_uniform.iloc[:, 0])
-        
+
         # Detailed results
         result_detailed = {
             "Coverage": coverage,
@@ -152,21 +153,25 @@ def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]
             "DGP": dgp_type,
             "level": level,
         }
-        
+
         # Group aggregation
         group_agg = dml_DiD.aggregate(aggregation="group")
         group_confint = group_agg.aggregated_frameworks.confint(level=level)
-        group_coverage = np.mean((group_confint.iloc[:, 0] < self.oracle_agg_group.values) & 
-                                (self.oracle_agg_group.values < group_confint.iloc[:, 1]))
+        group_coverage = np.mean(
+            (group_confint.iloc[:, 0] < self.oracle_agg_group.values)
+            & (self.oracle_agg_group.values < group_confint.iloc[:, 1])
+        )
         group_ci_length = np.mean(group_confint.iloc[:, 1] - group_confint.iloc[:, 0])
         group_bias = np.mean(abs(group_agg.aggregated_frameworks.thetas - self.oracle_agg_group.values))
-        
+
         group_agg.aggregated_frameworks.bootstrap(n_rep_boot=2000)
         group_confint_uniform = group_agg.aggregated_frameworks.confint(level=level, joint=True)
-        group_coverage_uniform = all((group_confint_uniform.iloc[:, 0] < self.oracle_agg_group.values) & 
-                                   (self.oracle_agg_group.values < group_confint_uniform.iloc[:, 1]))
+        group_coverage_uniform = all(
+            (group_confint_uniform.iloc[:, 0] < self.oracle_agg_group.values)
+            & (self.oracle_agg_group.values < group_confint_uniform.iloc[:, 1])
+        )
         group_ci_length_uniform = np.mean(group_confint_uniform.iloc[:, 1] - group_confint_uniform.iloc[:, 0])
-        
+
         # Aggregated results
         result_aggregated = {
             "Coverage": group_coverage,
@@ -181,51 +186,33 @@ def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]
             "DGP": dgp_type,
             "level": level,
         }
-        
-        # Store aggregated results
-        self.results_aggregated.append(result_aggregated)
-        
-        return result_detailed
-    
+
+        result = {
+            "detailed": result_detailed,
+            "aggregated": result_aggregated,
+        }
+
+        return result
+
     def summarize_results(self):
-        """Summarize results and save to CSV."""
-        df_results_detailed = pd.DataFrame(self.results)
-        df_results_aggregated = pd.DataFrame(self.results_aggregated)
-        
-        # Aggregate results by parameters
-        df_summary_detailed = df_results_detailed.groupby(
-            ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]
-        ).agg({
-            "Coverage": "mean",
-            "CI Length": "mean",
-            "Bias": "mean",
-            "Uniform Coverage": "mean",
-            "Uniform CI Length": "mean",
-            "repetition": "count"
-        }).reset_index()
-        
-        df_summary_aggregated = df_results_aggregated.groupby(
-            ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]
-        ).agg({
+        """Summarize the simulation results."""
+
+        groupby_cols = ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]
+        aggregation_dict = {
             "Coverage": "mean",
             "CI Length": "mean",
             "Bias": "mean",
             "Uniform Coverage": "mean",
             "Uniform CI Length": "mean",
-            "repetition": "count"
-        }).reset_index()
-        
-        # Save results
-        metadata = pd.DataFrame({
-            'DoubleML Version': [dml.__version__],
-            'Script': ['DIDMultiCoverageSimulation'],
-            'Date': [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
-            'Total Runtime (seconds)': [self.total_runtime],
-            'Python Version': [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"],
-        })
-        
-        df_summary_detailed.to_csv(f"{self.output_path}.csv", index=False)
-        df_summary_aggregated.to_csv(f"{self.output_path}_aggregated.csv", index=False)
-        metadata.to_csv(f"{self.output_path}_metadata.csv", index=False)
-        
-        return df_summary_detailed, df_summary_aggregated, metadata
\ No newline at end of file
+            "repetition": "count",
+        }
+
+        result_summary = dict()
+        for result_name, result_df in self.results.items():
+            result_summary[result_name] = (
+                result_df.groupby(groupby_cols)
+                .agg(aggregation_dict)
+                .reset_index()
+            )
+
+        return result_summary
diff --git a/scripts/did/did_pa_multi_agg_coverage.py b/scripts/did/did_pa_multi_agg_coverage.py
index e69de29..76c35af 100644
--- a/scripts/did/did_pa_multi_agg_coverage.py
+++ b/scripts/did/did_pa_multi_agg_coverage.py
@@ -0,0 +1,9 @@
+from montecover.did import DIDMultiCoverageSimulation
+
+simulation = DIDMultiCoverageSimulation(
+    repetitions=5,
+    n_obs=500,
+    output_path="results/did/did_pa_multi_coverage"
+)
+simulation.run_simulation()
+simulation.save_results()
\ No newline at end of file

From fbb40f313fc5d7f9a44f76ebbbd8fff63f04939d Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 13:51:23 +0100
Subject: [PATCH 07/60] add all aggregations

---
 monte-cover/src/montecover/base.py          |  22 ++++
 monte-cover/src/montecover/did/did_multi.py | 120 +++++++-------------
 2 files changed, 63 insertions(+), 79 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index dd2a7ee..18ff049 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -115,3 +115,25 @@ def save_results(self):
         for df_name, df in self.result_summary.items():
             df.to_csv(f"{self.output_path}_{df_name}.csv", index=False)
         metadata.to_csv(f"{self.output_path}_metadata.csv", index=False)
+
+    @staticmethod
+    def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
+        """Compute coverage, CI length, and bias."""
+
+        coverage = np.mean((confint.iloc[:, 0] < oracle_thetas) & (oracle_thetas < confint.iloc[:, 1]))
+        ci_length = np.mean(confint.iloc[:, 1] - confint.iloc[:, 0])
+        bias = np.mean(abs(thetas - oracle_thetas))
+        result_dict = {
+            "Coverage": coverage,
+            "CI Length": ci_length,
+            "Bias": bias,
+        }
+
+        if joint_confint is not None:
+            coverage_uniform = all((joint_confint.iloc[:, 0] < oracle_thetas) & (oracle_thetas < joint_confint.iloc[:, 1]))
+            ci_length_uniform = np.mean(joint_confint.iloc[:, 1] - joint_confint.iloc[:, 0])
+
+            result_dict["Uniform Coverage"] = coverage_uniform
+            result_dict["Uniform CI Length"] = ci_length_uniform
+
+        return result_dict
diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
index f63f964..f0f9430 100644
--- a/monte-cover/src/montecover/did/did_multi.py
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -38,31 +38,25 @@ def __init__(
 
     def _calculate_oracle_values(self):
         """Calculate oracle values for the simulation."""
+
+        self.oracle_values = dict()
         # Oracle values
         df_oracle = make_did_CS2021(n_obs=int(1e6), dgp_type=1)  # does not depend on the DGP type
         df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"]
-        self.oracle_thetas = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
-        print("ATTs:")
-        print(self.oracle_thetas)
+        self.oracle_values["detailed"] = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
 
         # Oracle group aggregation
         df_oracle_post_treatment = df_oracle[df_oracle["t"] >= df_oracle["d"]]
-        self.oracle_agg_group = df_oracle_post_treatment.groupby("d")["ite"].mean()
-        print("Group aggregated ATTs:")
-        print(self.oracle_agg_group)
+        self.oracle_values["group"] = df_oracle_post_treatment.groupby("d")["ite"].mean()
 
         # Oracle time aggregation
-        self.oracle_agg_time = df_oracle_post_treatment.groupby("t")["ite"].mean()
-        print("Time aggregated ATTs:")
-        print(self.oracle_agg_time)
+        self.oracle_values["time"] = df_oracle_post_treatment.groupby("t")["ite"].mean()
 
         # Oracle eventstudy aggregation
         df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype("datetime64[M]") - pd.to_datetime(
             df_oracle["d"]
         ).values.astype("datetime64[M]")
-        self.oracle_agg_eventstudy = df_oracle.groupby("e")["ite"].mean()
-        print("Event Study aggregated ATTs:")
-        print(self.oracle_agg_eventstudy)
+        self.oracle_values["eventstudy"] = df_oracle.groupby("e")["ite"].mean()[1:]
 
     def setup_parameters(self) -> Dict[str, List[Any]]:
         """Define simulation parameters."""
@@ -118,79 +112,44 @@ def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
 
         # Fit the model
         dml_DiD.fit(n_jobs_cv=5)
+        dml_DiD.bootstrap(n_rep_boot=2000)
 
         # Oracle values for this model
-        theta = np.full_like(dml_DiD.coef, np.nan)
+        oracle_thetas = np.full_like(dml_DiD.coef, np.nan)
         for i, (g, _, t) in enumerate(dml_DiD.gt_combinations):
-            group_index = self.oracle_thetas["d"] == g
-            time_index = self.oracle_thetas["t"] == t
-            theta[i] = self.oracle_thetas[group_index & time_index]["ite"].iloc[0]
+            group_index = self.oracle_values["detailed"]["d"] == g
+            time_index = self.oracle_values["detailed"]["t"] == t
+            oracle_thetas[i] = self.oracle_values["detailed"][group_index & time_index]["ite"].iloc[0]
+
+        result = dict()
+        result["detailed"] = self._compute_coverage(
+            thetas=dml_DiD.coef,
+            oracle_thetas=oracle_thetas,
+            confint=dml_DiD.confint(level=level),
+            joint_confint=dml_DiD.confint(level=level, joint=True),
+            )
 
-        # Confidence intervals and metrics
-        confint = dml_DiD.confint(level=level)
-        coverage = np.mean((confint.iloc[:, 0] < theta) & (theta < confint.iloc[:, 1]))
-        ci_length = np.mean(confint.iloc[:, 1] - confint.iloc[:, 0])
-        bias = np.mean(abs(dml_DiD.coef - theta))
+        for aggregation_method in ["group", "time", "eventstudy"]:
+            agg_obj = dml_DiD.aggregate(aggregation=aggregation_method)
+            agg_obj.aggregated_frameworks.bootstrap(n_rep_boot=2000)
 
-        # Bootstrap for uniform confidence intervals
-        dml_DiD.bootstrap(n_rep_boot=2000)
-        confint_uniform = dml_DiD.confint(level=level, joint=True)
-
-        coverage_uniform = all((confint_uniform.iloc[:, 0] < theta) & (theta < confint_uniform.iloc[:, 1]))
-        ci_length_uniform = np.mean(confint_uniform.iloc[:, 1] - confint_uniform.iloc[:, 0])
-
-        # Detailed results
-        result_detailed = {
-            "Coverage": coverage,
-            "CI Length": ci_length,
-            "Bias": bias,
-            "Uniform Coverage": coverage_uniform,
-            "Uniform CI Length": ci_length_uniform,
-            "Learner g": learner_g_name,
-            "Learner m": learner_m_name,
-            "Score": score,
-            "In-sample-norm.": in_sample_normalization,
-            "DGP": dgp_type,
-            "level": level,
-        }
-
-        # Group aggregation
-        group_agg = dml_DiD.aggregate(aggregation="group")
-        group_confint = group_agg.aggregated_frameworks.confint(level=level)
-        group_coverage = np.mean(
-            (group_confint.iloc[:, 0] < self.oracle_agg_group.values)
-            & (self.oracle_agg_group.values < group_confint.iloc[:, 1])
-        )
-        group_ci_length = np.mean(group_confint.iloc[:, 1] - group_confint.iloc[:, 0])
-        group_bias = np.mean(abs(group_agg.aggregated_frameworks.thetas - self.oracle_agg_group.values))
-
-        group_agg.aggregated_frameworks.bootstrap(n_rep_boot=2000)
-        group_confint_uniform = group_agg.aggregated_frameworks.confint(level=level, joint=True)
-        group_coverage_uniform = all(
-            (group_confint_uniform.iloc[:, 0] < self.oracle_agg_group.values)
-            & (self.oracle_agg_group.values < group_confint_uniform.iloc[:, 1])
-        )
-        group_ci_length_uniform = np.mean(group_confint_uniform.iloc[:, 1] - group_confint_uniform.iloc[:, 0])
-
-        # Aggregated results
-        result_aggregated = {
-            "Coverage": group_coverage,
-            "CI Length": group_ci_length,
-            "Bias": group_bias,
-            "Uniform Coverage": group_coverage_uniform,
-            "Uniform CI Length": group_ci_length_uniform,
-            "Learner g": learner_g_name,
-            "Learner m": learner_m_name,
-            "Score": score,
-            "In-sample-norm.": in_sample_normalization,
-            "DGP": dgp_type,
-            "level": level,
-        }
+            result[aggregation_method] = self._compute_coverage(
+                thetas=agg_obj.aggregated_frameworks.thetas,
+                oracle_thetas=self.oracle_values[aggregation_method].values,
+                confint=agg_obj.aggregated_frameworks.confint(level=level),
+                joint_confint=agg_obj.aggregated_frameworks.confint(level=level, joint=True),
+            )
 
-        result = {
-            "detailed": result_detailed,
-            "aggregated": result_aggregated,
-        }
+        # add parameters to the result
+        for result_dict in result.values():
+            result_dict.update({
+                "Learner g": learner_g_name,
+                "Learner m": learner_m_name,
+                "Score": score,
+                "In-sample-norm.": in_sample_normalization,
+                "DGP": dgp_type,
+                "level": level,
+            })
 
         return result
 
@@ -216,3 +175,6 @@ def summarize_results(self):
             )
 
         return result_summary
+
+
+

From b3175f65dc3c8439eb716a5c03efb8b02bc416a5 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 14:02:00 +0100
Subject: [PATCH 08/60] change output path to save_results method

---
 monte-cover/src/montecover/base.py          |  8 ++---
 monte-cover/src/montecover/did/did_multi.py | 33 ++++++++++-----------
 scripts/did/did_pa_multi_agg_coverage.py    |  3 +-
 3 files changed, 20 insertions(+), 24 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 18ff049..072eadb 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -19,13 +19,11 @@ def __init__(
         repetitions: int = 20,
         max_runtime: float = 5.5 * 3600,
         random_seed: int = 42,
-        output_path: str = "results",
         suppress_warnings: bool = True,
     ):
         self.repetitions = repetitions
         self.max_runtime = max_runtime
         self.random_seed = random_seed
-        self.output_path = output_path
         self.suppress_warnings = suppress_warnings
 
         # Results storage
@@ -100,7 +98,7 @@ def run_simulation(self):
         self.result_summary = self.summarize_results()
         print("Simulation finished.")
 
-    def save_results(self):
+    def save_results(self, output_path: str = "results", file_prefix: str = ""):
         """Save the simulation results."""
         metadata = pd.DataFrame(
             {
@@ -113,8 +111,8 @@ def save_results(self):
         )
 
         for df_name, df in self.result_summary.items():
-            df.to_csv(f"{self.output_path}_{df_name}.csv", index=False)
-        metadata.to_csv(f"{self.output_path}_metadata.csv", index=False)
+            df.to_csv(f"{output_path}{file_prefix}_{df_name}.csv", index=False)
+        metadata.to_csv(f"{output_path}{file_prefix}_metadata.csv", index=False)
 
     @staticmethod
     def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
index f0f9430..3aa84e6 100644
--- a/monte-cover/src/montecover/did/did_multi.py
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -14,7 +14,6 @@ class DIDMultiCoverageSimulation(BaseSimulation):
 
     def __init__(
         self,
-        output_path: str,
         repetitions: int = 20,
         n_obs: int = 500,
         max_runtime: float = 5.5 * 3600,
@@ -25,7 +24,6 @@ def __init__(
             repetitions=repetitions,
             max_runtime=max_runtime,
             random_seed=random_seed,
-            output_path=output_path,
             suppress_warnings=suppress_warnings,
         )
         self.n_obs = n_obs
@@ -79,21 +77,12 @@ def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
         learner_m_name, ml_m = params["learner_m"]
         level = params["level"]
 
-        # Generate data
-        data = make_did_CS2021(n_obs=self.n_obs, dgp_type=dgp_type)
-        obj_dml_data = dml.data.DoubleMLPanelData(
-            data,
-            y_col="y",
-            d_cols="d",
-            id_col="id",
-            t_col="t",
-            x_cols=["Z1", "Z2", "Z3", "Z4"],
-        )
-
+        dml_data = self._generate_data(dgp_type=dgp_type)
+        
         # Fit model
         if score == "experimental":
             dml_DiD = dml.did.DoubleMLDIDMulti(
-                obj_dml_data=obj_dml_data,
+                obj_dml_data=dml_data,
                 ml_g=ml_g,
                 ml_m=None,
                 gt_combinations="standard",
@@ -102,7 +91,7 @@ def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
             )
         else:
             dml_DiD = dml.did.DoubleMLDIDMulti(
-                obj_dml_data=obj_dml_data,
+                obj_dml_data=dml_data,
                 ml_g=ml_g,
                 ml_m=ml_m,
                 gt_combinations="standard",
@@ -176,5 +165,15 @@ def summarize_results(self):
 
         return result_summary
 
-
-
+    def _generate_data(self, dgp_type: int) -> dml.data.DoubleMLPanelData:
+        """Generate data for the simulation."""
+        data = make_did_CS2021(n_obs=self.n_obs, dgp_type=dgp_type)
+        dml_data = dml.data.DoubleMLPanelData(
+            data,
+            y_col="y",
+            d_cols="d",
+            id_col="id",
+            t_col="t",
+            x_cols=["Z1", "Z2", "Z3", "Z4"],
+        )
+        return dml_data
diff --git a/scripts/did/did_pa_multi_agg_coverage.py b/scripts/did/did_pa_multi_agg_coverage.py
index 76c35af..01d95db 100644
--- a/scripts/did/did_pa_multi_agg_coverage.py
+++ b/scripts/did/did_pa_multi_agg_coverage.py
@@ -3,7 +3,6 @@
 simulation = DIDMultiCoverageSimulation(
     repetitions=5,
     n_obs=500,
-    output_path="results/did/did_pa_multi_coverage"
 )
 simulation.run_simulation()
-simulation.save_results()
\ No newline at end of file
+simulation.save_results(output_path="results/did/", file_prefix="did_pa_multi_coverage")
\ No newline at end of file

From a8240ece098c44fd2bdef27d04173645e1acbd72 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 14:08:40 +0100
Subject: [PATCH 09/60] more concise did model

---
 monte-cover/src/montecover/did/did_multi.py | 46 ++++++++-------------
 1 file changed, 17 insertions(+), 29 deletions(-)

diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
index 3aa84e6..d6d0489 100644
--- a/monte-cover/src/montecover/did/did_multi.py
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -78,48 +78,36 @@ def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
         level = params["level"]
 
         dml_data = self._generate_data(dgp_type=dgp_type)
-        
-        # Fit model
-        if score == "experimental":
-            dml_DiD = dml.did.DoubleMLDIDMulti(
-                obj_dml_data=dml_data,
-                ml_g=ml_g,
-                ml_m=None,
-                gt_combinations="standard",
-                score=score,
-                in_sample_normalization=in_sample_normalization,
-            )
-        else:
-            dml_DiD = dml.did.DoubleMLDIDMulti(
-                obj_dml_data=dml_data,
-                ml_g=ml_g,
-                ml_m=ml_m,
-                gt_combinations="standard",
-                score=score,
-                in_sample_normalization=in_sample_normalization,
-            )
 
-        # Fit the model
-        dml_DiD.fit(n_jobs_cv=5)
-        dml_DiD.bootstrap(n_rep_boot=2000)
+        # Model
+        dml_model = dml.did.DoubleMLDIDMulti(
+            obj_dml_data=dml_data,
+            ml_g=ml_g,
+            ml_m=None if score == "experimental" else ml_m,
+            gt_combinations="standard",
+            score=score,
+            in_sample_normalization=in_sample_normalization,
+        )
+        dml_model.fit(n_jobs_cv=5)
+        dml_model.bootstrap(n_rep_boot=2000)
 
         # Oracle values for this model
-        oracle_thetas = np.full_like(dml_DiD.coef, np.nan)
-        for i, (g, _, t) in enumerate(dml_DiD.gt_combinations):
+        oracle_thetas = np.full_like(dml_model.coef, np.nan)
+        for i, (g, _, t) in enumerate(dml_model.gt_combinations):
             group_index = self.oracle_values["detailed"]["d"] == g
             time_index = self.oracle_values["detailed"]["t"] == t
             oracle_thetas[i] = self.oracle_values["detailed"][group_index & time_index]["ite"].iloc[0]
 
         result = dict()
         result["detailed"] = self._compute_coverage(
-            thetas=dml_DiD.coef,
+            thetas=dml_model.coef,
             oracle_thetas=oracle_thetas,
-            confint=dml_DiD.confint(level=level),
-            joint_confint=dml_DiD.confint(level=level, joint=True),
+            confint=dml_model.confint(level=level),
+            joint_confint=dml_model.confint(level=level, joint=True),
             )
 
         for aggregation_method in ["group", "time", "eventstudy"]:
-            agg_obj = dml_DiD.aggregate(aggregation=aggregation_method)
+            agg_obj = dml_model.aggregate(aggregation=aggregation_method)
             agg_obj.aggregated_frameworks.bootstrap(n_rep_boot=2000)
 
             result[aggregation_method] = self._compute_coverage(

From 097f167fae448507ca4a376499aeb54119f015bb Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 14:47:42 +0100
Subject: [PATCH 10/60] add logs to gitignore

---
 .gitignore | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.gitignore b/.gitignore
index c18dd8d..67d3ba2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,6 @@
 __pycache__/
+
+# Logs
+monte-cover/logs/
+*.log
+run_*.out
\ No newline at end of file

From 7bc892e187e4af81378607ddd232c7a00d92c45c Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 14:58:13 +0100
Subject: [PATCH 11/60] update with config files

---
 monte-cover/pyproject.toml                  |   1 +
 monte-cover/src/montecover/base.py          | 189 ++++++++++++++++----
 monte-cover/src/montecover/did/did_multi.py |  91 ++++++----
 monte-cover/uv.lock                         |  28 +++
 scripts/did/did_pa_multi_agg_coverage.py    |  18 +-
 scripts/did/multi_config.yml                |  24 +++
 6 files changed, 273 insertions(+), 78 deletions(-)
 create mode 100644 scripts/did/multi_config.yml

diff --git a/monte-cover/pyproject.toml b/monte-cover/pyproject.toml
index 6aee923..357e36c 100644
--- a/monte-cover/pyproject.toml
+++ b/monte-cover/pyproject.toml
@@ -16,6 +16,7 @@ dependencies = [
     "lightgbm>=4.6.0",
     "numpy>=2.2.4",
     "pandas>=2.2.3",
+    "pyyaml>=6.0.2",
     "ruff>=0.11.0",
     "scikit-learn>=1.5.2",
 ]
diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 072eadb..cba4e8f 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -1,14 +1,17 @@
+import logging
+import os
+import sys
 import time
 import warnings
-import pandas as pd
 from abc import ABC, abstractmethod
+from datetime import datetime
 from itertools import product
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
-import sys
-from datetime import datetime
-import numpy as np
 import doubleml as dml
+import numpy as np
+import pandas as pd
+import yaml
 
 
 class BaseSimulation(ABC):
@@ -16,16 +19,25 @@ class BaseSimulation(ABC):
 
     def __init__(
         self,
-        repetitions: int = 20,
-        max_runtime: float = 5.5 * 3600,
-        random_seed: int = 42,
+        config_file: str,
         suppress_warnings: bool = True,
+        log_level: str = "INFO",
+        log_file: Optional[str] = None,
     ):
-        self.repetitions = repetitions
-        self.max_runtime = max_runtime
-        self.random_seed = random_seed
+        # Load config first to get default values if needed
+        self.config_file = config_file
+        self.config = self._load_config(config_file)
+
+        # Apply parameters from config
+        self.repetitions = self.config.get("repetitions", 20)
+        self.max_runtime = self.config.get("max_runtime", 5.5 * 3600)
+        self.random_seed = self.config.get("random_seed", 42)
         self.suppress_warnings = suppress_warnings
 
+        # Set up logging
+        self._setup_logging(log_level, log_file)
+        self.logger.info(f"Loaded configuration from {config_file}")
+
         # Results storage
         self.results = dict()
         self.result_summary = None
@@ -40,14 +52,13 @@ def __init__(
             warnings.simplefilter(action="ignore", category=UserWarning)
 
         np.random.seed(self.random_seed)
+        self.logger.info(f"Initialized simulation with random seed {self.random_seed}")
 
-    @abstractmethod
-    def setup_parameters(self) -> Dict[str, List[Any]]:
-        """Define simulation parameters."""
-        pass
+        # Let child classes process any specific parameters
+        self._process_config_parameters()
 
     @abstractmethod
-    def run_single_rep(self, rep_idx: int, params: Dict[str, Any]) -> Dict[str, Any]:
+    def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
         """Run a single repetition with the given parameters."""
         pass
 
@@ -56,36 +67,75 @@ def summarize_results(self) -> Dict[str, Any]:
         """Summarize the simulation results."""
         pass
 
+    def get_parameters(self) -> Dict[str, List[Any]]:
+        """Get parameter grid from config."""
+        param_grid = self.config.get("parameters", {})
+        if not param_grid:
+            self.logger.warning("No parameter grid found in config. Using empty parameter set.")
+        return param_grid
+
     def run_simulation(self):
         """Run the full simulation."""
         self.start_time = time.time()
-        param_grid = self.setup_parameters()
+        self.logger.info("Starting simulation")
+
+        param_grid = self.get_parameters()
+        self.logger.info(f"Parameter grid: {param_grid}")
+
+        # Calculate total number of parameter combinations
+        total_combinations = np.prod([len(values) for values in param_grid.values()])
+        self.logger.info(f"Total parameter combinations: {total_combinations}")
+
+        # Calculate expected total iterations
+        total_iterations = total_combinations * self.repetitions
+        self.logger.info(f"Expected total iterations: {total_iterations}")
 
         # Loop through repetitions
         for i_rep in range(self.repetitions):
-            print(f"Repetition: {i_rep + 1}/{self.repetitions}", end="\r")
+            rep_start_time = time.time()
+            self.logger.info(f"Starting repetition {i_rep + 1}/{self.repetitions}")
 
             # Check elapsed time
             elapsed_time = time.time() - self.start_time
             if elapsed_time > self.max_runtime:
-                print("Maximum runtime exceeded. Stopping the simulation.")
+                self.logger.warning("Maximum runtime exceeded. Stopping the simulation.")
                 break
 
+            param_combo = 0
             keys = param_grid.keys()
             for values in product(*param_grid.values()):
+                param_combo += 1
                 params = dict(zip(keys, values))
-                repetition_results = self.run_single_rep(params)
-                if repetition_results is not None:
-                    assert isinstance(repetition_results, dict), "The result must be a dictionary."
-                    # Process each dataframe in the result dictionary
-                    for result_name, repetition_result in repetition_results.items():
-                        assert isinstance(repetition_result, dict), "Each result must be a dictionary."
-                        repetition_result["repetition"] = i_rep
-
-                        # Initialize key in results dict if not exists
-                        if result_name not in self.results:
-                            self.results[result_name] = []
-                        self.results[result_name].append(repetition_result)
+                param_start_time = time.time()
+
+                # Log parameter combination
+                self.logger.debug(f"Rep {i_rep+1}, Combo {param_combo}/{total_combinations}: {params}")
+
+                try:
+                    repetition_results = self.run_single_rep(params)
+                    param_end_time = time.time()
+                    param_duration = param_end_time - param_start_time
+
+                    if repetition_results is not None:
+                        assert isinstance(repetition_results, dict), "The result must be a dictionary."
+                        # Process each dataframe in the result dictionary
+                        for result_name, repetition_result in repetition_results.items():
+                            assert isinstance(repetition_result, dict), "Each result must be a dictionary."
+                            repetition_result["repetition"] = i_rep
+
+                            # Initialize key in results dict if not exists
+                            if result_name not in self.results:
+                                self.results[result_name] = []
+                            self.results[result_name].append(repetition_result)
+
+                        self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s")
+                except Exception as e:
+                    self.logger.error(f"Error in repetition {i_rep+1}, parameters {params}: {str(e)}")
+                    self.logger.exception("Exception details:")
+
+            rep_end_time = time.time()
+            rep_duration = rep_end_time - rep_start_time
+            self.logger.info(f"Repetition {i_rep+1} completed in {rep_duration:.2f}s")
 
         # convert results to dataframes
         for key, value in self.results.items():
@@ -93,26 +143,48 @@ def run_simulation(self):
 
         self.end_time = time.time()
         self.total_runtime = self.end_time - self.start_time
+        self.logger.info(f"Simulation completed in {self.total_runtime:.2f}s")
 
         # Summarize & save results
+        self.logger.info("Summarizing results")
         self.result_summary = self.summarize_results()
-        print("Simulation finished.")
 
     def save_results(self, output_path: str = "results", file_prefix: str = ""):
         """Save the simulation results."""
+        os.makedirs(output_path, exist_ok=True)
+
         metadata = pd.DataFrame(
             {
                 "DoubleML Version": [dml.__version__],
-                "Script": ["DIDMultiCoverageSimulation"],
-                "Date": [datetime.now().strftime("%Y-%m-%d %H")],
+                "Script": [self.__class__.__name__],
+                "Date": [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
                 "Total Runtime (seconds)": [self.total_runtime],
                 "Python Version": [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"],
+                "Config File": [self.config_file],
             }
         )
 
         for df_name, df in self.result_summary.items():
-            df.to_csv(f"{output_path}{file_prefix}_{df_name}.csv", index=False)
-        metadata.to_csv(f"{output_path}{file_prefix}_metadata.csv", index=False)
+            output_file = os.path.join(output_path, f"{file_prefix}_{df_name}.csv")
+            df.to_csv(output_file, index=False)
+            self.logger.info(f"Results saved to {output_file}")
+
+        metadata_file = os.path.join(output_path, f"{file_prefix}_metadata.csv")
+        metadata.to_csv(metadata_file, index=False)
+        self.logger.info(f"Metadata saved to {metadata_file}")
+
+    def save_config(self, output_path: str):
+        """Save the current configuration to a YAML file."""
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+        if not (output_path.endswith(".yaml") or output_path.endswith(".yml")):
+            output_path += ".yaml"
+            self.logger.warning(f"Adding .yaml extension to output path: {output_path}")
+
+        with open(output_path, "w") as file:
+            yaml.dump(self.config, file)
+
+        self.logger.info(f"Configuration saved to {output_path}")
 
     @staticmethod
     def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
@@ -135,3 +207,48 @@ def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
             result_dict["Uniform CI Length"] = ci_length_uniform
 
         return result_dict
+
+    def _load_config(self, config_path: str) -> Dict[str, Any]:
+        """Load configuration from a YAML file."""
+        if not os.path.exists(config_path):
+            raise FileNotFoundError(f"Config file not found: {config_path}")
+
+        if config_path.endswith(".yaml") or config_path.endswith(".yml"):
+            with open(config_path, "r") as file:
+                config = yaml.safe_load(file)
+        else:
+            raise ValueError(f"Unsupported config file format: {config_path}. Use .yaml or .yml")
+
+        return config
+
+    def _process_config_parameters(self):
+        """
+        Process any special configuration parameters.
+        Child classes should override this method to handle specific parameters.
+        """
+        pass
+
+    def _setup_logging(self, log_level: str, log_file: Optional[str]):
+        """Set up logging configuration."""
+        level = getattr(logging, log_level.upper())
+        self.logger = logging.getLogger(self.__class__.__name__)
+        self.logger.setLevel(level)
+
+        # Clear any existing handlers
+        if self.logger.hasHandlers():
+            self.logger.handlers.clear()
+
+        # Console handler
+        ch = logging.StreamHandler()
+        ch.setLevel(level)
+        formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+        ch.setFormatter(formatter)
+        self.logger.addHandler(ch)
+
+        # File handler if specified
+        if log_file:
+            os.makedirs(os.path.dirname(log_file), exist_ok=True)
+            fh = logging.FileHandler(log_file)
+            fh.setLevel(level)
+            fh.setFormatter(formatter)
+            self.logger.addHandler(fh)
diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
index d6d0489..68ce5f0 100644
--- a/monte-cover/src/montecover/did/did_multi.py
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, Optional
 
 import doubleml as dml
 import numpy as np
@@ -14,28 +14,56 @@ class DIDMultiCoverageSimulation(BaseSimulation):
 
     def __init__(
         self,
-        repetitions: int = 20,
-        n_obs: int = 500,
-        max_runtime: float = 5.5 * 3600,
-        random_seed: int = 42,
+        config_file: str,
         suppress_warnings: bool = True,
+        log_level: str = "INFO",
+        log_file: Optional[str] = None,
     ):
         super().__init__(
-            repetitions=repetitions,
-            max_runtime=max_runtime,
-            random_seed=random_seed,
+            config_file=config_file,
             suppress_warnings=suppress_warnings,
+            log_level=log_level,
+            log_file=log_file,
         )
-        self.n_obs = n_obs
+
+        # Additional results storage for aggregated results
+        self.results_aggregated = []
 
         # Calculate oracle values
         self._calculate_oracle_values()
 
-        # Additional results storage for aggregated results
-        self.results_aggregated = []
+    def _process_config_parameters(self):
+        """Process simulation-specific parameters from config"""
+        # Extract n_obs from config or use default
+        self.n_obs = self.config.get("n_obs", 500)
+
+        # Process ML models in parameter grid
+        if "parameters" in self.config:
+            param_grid = self.config["parameters"]
+
+            # Convert learner_g strings to actual objects
+            if "learner_g" in param_grid:
+                processed_learners_g = []
+                for learner in param_grid["learner_g"]:
+                    if isinstance(learner, list) and len(learner) == 2:
+                        if learner[0] == "Linear":
+                            processed_learners_g.append(("Linear", LinearRegression()))
+                        # Add more model types as needed
+                param_grid["learner_g"] = processed_learners_g
+
+            # Convert learner_m strings to actual objects
+            if "learner_m" in param_grid:
+                processed_learners_m = []
+                for learner in param_grid["learner_m"]:
+                    if isinstance(learner, list) and len(learner) == 2:
+                        if learner[0] == "Linear":
+                            processed_learners_m.append(("Linear", LogisticRegression()))
+                        # Add more model types as needed
+                param_grid["learner_m"] = processed_learners_m
 
     def _calculate_oracle_values(self):
         """Calculate oracle values for the simulation."""
+        self.logger.info("Calculating oracle values")
 
         self.oracle_values = dict()
         # Oracle values
@@ -56,17 +84,6 @@ def _calculate_oracle_values(self):
         ).values.astype("datetime64[M]")
         self.oracle_values["eventstudy"] = df_oracle.groupby("e")["ite"].mean()[1:]
 
-    def setup_parameters(self) -> Dict[str, List[Any]]:
-        """Define simulation parameters."""
-        return {
-            "DGP": [1, 4, 6],
-            "score": ["observational", "experimental"],
-            "in_sample_normalization": [True, False],
-            "learner_g": [("Linear", LinearRegression())],
-            "learner_m": [("Linear", LogisticRegression())],
-            "level": [0.95, 0.90],
-        }
-
     def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
         """Run a single repetition with the given parameters."""
         # Extract parameters
@@ -77,6 +94,8 @@ def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
         learner_m_name, ml_m = params["learner_m"]
         level = params["level"]
 
+        self.logger.debug(f"Running with DGP={dgp_type}, score={score}, level={level}")
+
         dml_data = self._generate_data(dgp_type=dgp_type)
 
         # Model
@@ -104,7 +123,7 @@ def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
             oracle_thetas=oracle_thetas,
             confint=dml_model.confint(level=level),
             joint_confint=dml_model.confint(level=level, joint=True),
-            )
+        )
 
         for aggregation_method in ["group", "time", "eventstudy"]:
             agg_obj = dml_model.aggregate(aggregation=aggregation_method)
@@ -119,19 +138,22 @@ def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
 
         # add parameters to the result
         for result_dict in result.values():
-            result_dict.update({
-                "Learner g": learner_g_name,
-                "Learner m": learner_m_name,
-                "Score": score,
-                "In-sample-norm.": in_sample_normalization,
-                "DGP": dgp_type,
-                "level": level,
-            })
+            result_dict.update(
+                {
+                    "Learner g": learner_g_name,
+                    "Learner m": learner_m_name,
+                    "Score": score,
+                    "In-sample-norm.": in_sample_normalization,
+                    "DGP": dgp_type,
+                    "level": level,
+                }
+            )
 
         return result
 
     def summarize_results(self):
         """Summarize the simulation results."""
+        self.logger.info("Summarizing simulation results")
 
         groupby_cols = ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]
         aggregation_dict = {
@@ -145,11 +167,8 @@ def summarize_results(self):
 
         result_summary = dict()
         for result_name, result_df in self.results.items():
-            result_summary[result_name] = (
-                result_df.groupby(groupby_cols)
-                .agg(aggregation_dict)
-                .reset_index()
-            )
+            result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index()
+            self.logger.debug(f"Summarized {result_name} results")
 
         return result_summary
 
diff --git a/monte-cover/uv.lock b/monte-cover/uv.lock
index eed1b7c..57e1dd3 100644
--- a/monte-cover/uv.lock
+++ b/monte-cover/uv.lock
@@ -511,6 +511,7 @@ dependencies = [
     { name = "lightgbm" },
     { name = "numpy" },
     { name = "pandas" },
+    { name = "pyyaml" },
     { name = "ruff" },
     { name = "scikit-learn" },
 ]
@@ -525,6 +526,7 @@ requires-dist = [
     { name = "lightgbm", specifier = ">=4.6.0" },
     { name = "numpy", specifier = ">=2.2.4" },
     { name = "pandas", specifier = ">=2.2.3" },
+    { name = "pyyaml", specifier = ">=6.0.2" },
     { name = "ruff", specifier = ">=0.11.0" },
     { name = "scikit-learn", specifier = ">=1.5.2" },
 ]
@@ -862,6 +864,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b4/f4/f785020090fb050e7fb6d34b780f2231f302609dc964672f72bfaeb59a28/pywin32-310-cp313-cp313-win_arm64.whl", hash = "sha256:e308f831de771482b7cf692a1f308f8fca701b2d8f9dde6cc440c7da17e47b33", size = 8458152 },
 ]
 
+[[package]]
+name = "pyyaml"
+version = "6.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/54/ed/79a089b6be93607fa5cdaedf301d7dfb23af5f25c398d5ead2525b063e17/pyyaml-6.0.2.tar.gz", hash = "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e", size = 130631 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/86/0c/c581167fc46d6d6d7ddcfb8c843a4de25bdd27e4466938109ca68492292c/PyYAML-6.0.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c70c95198c015b85feafc136515252a261a84561b7b1d51e3384e0655ddf25ab", size = 183873 },
+    { url = "https://files.pythonhosted.org/packages/a8/0c/38374f5bb272c051e2a69281d71cba6fdb983413e6758b84482905e29a5d/PyYAML-6.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ce826d6ef20b1bc864f0a68340c8b3287705cae2f8b4b1d932177dcc76721725", size = 173302 },
+    { url = "https://files.pythonhosted.org/packages/c3/93/9916574aa8c00aa06bbac729972eb1071d002b8e158bd0e83a3b9a20a1f7/PyYAML-6.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f71ea527786de97d1a0cc0eacd1defc0985dcf6b3f17bb77dcfc8c34bec4dc5", size = 739154 },
+    { url = "https://files.pythonhosted.org/packages/95/0f/b8938f1cbd09739c6da569d172531567dbcc9789e0029aa070856f123984/PyYAML-6.0.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9b22676e8097e9e22e36d6b7bda33190d0d400f345f23d4065d48f4ca7ae0425", size = 766223 },
+    { url = "https://files.pythonhosted.org/packages/b9/2b/614b4752f2e127db5cc206abc23a8c19678e92b23c3db30fc86ab731d3bd/PyYAML-6.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:80bab7bfc629882493af4aa31a4cfa43a4c57c83813253626916b8c7ada83476", size = 767542 },
+    { url = "https://files.pythonhosted.org/packages/d4/00/dd137d5bcc7efea1836d6264f049359861cf548469d18da90cd8216cf05f/PyYAML-6.0.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:0833f8694549e586547b576dcfaba4a6b55b9e96098b36cdc7ebefe667dfed48", size = 731164 },
+    { url = "https://files.pythonhosted.org/packages/c9/1f/4f998c900485e5c0ef43838363ba4a9723ac0ad73a9dc42068b12aaba4e4/PyYAML-6.0.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8b9c7197f7cb2738065c481a0461e50ad02f18c78cd75775628afb4d7137fb3b", size = 756611 },
+    { url = "https://files.pythonhosted.org/packages/df/d1/f5a275fdb252768b7a11ec63585bc38d0e87c9e05668a139fea92b80634c/PyYAML-6.0.2-cp312-cp312-win32.whl", hash = "sha256:ef6107725bd54b262d6dedcc2af448a266975032bc85ef0172c5f059da6325b4", size = 140591 },
+    { url = "https://files.pythonhosted.org/packages/0c/e8/4f648c598b17c3d06e8753d7d13d57542b30d56e6c2dedf9c331ae56312e/PyYAML-6.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:7e7401d0de89a9a855c839bc697c079a4af81cf878373abd7dc625847d25cbd8", size = 156338 },
+    { url = "https://files.pythonhosted.org/packages/ef/e3/3af305b830494fa85d95f6d95ef7fa73f2ee1cc8ef5b495c7c3269fb835f/PyYAML-6.0.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:efdca5630322a10774e8e98e1af481aad470dd62c3170801852d752aa7a783ba", size = 181309 },
+    { url = "https://files.pythonhosted.org/packages/45/9f/3b1c20a0b7a3200524eb0076cc027a970d320bd3a6592873c85c92a08731/PyYAML-6.0.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:50187695423ffe49e2deacb8cd10510bc361faac997de9efef88badc3bb9e2d1", size = 171679 },
+    { url = "https://files.pythonhosted.org/packages/7c/9a/337322f27005c33bcb656c655fa78325b730324c78620e8328ae28b64d0c/PyYAML-6.0.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0ffe8360bab4910ef1b9e87fb812d8bc0a308b0d0eef8c8f44e0254ab3b07133", size = 733428 },
+    { url = "https://files.pythonhosted.org/packages/a3/69/864fbe19e6c18ea3cc196cbe5d392175b4cf3d5d0ac1403ec3f2d237ebb5/PyYAML-6.0.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:17e311b6c678207928d649faa7cb0d7b4c26a0ba73d41e99c4fff6b6c3276484", size = 763361 },
+    { url = "https://files.pythonhosted.org/packages/04/24/b7721e4845c2f162d26f50521b825fb061bc0a5afcf9a386840f23ea19fa/PyYAML-6.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:70b189594dbe54f75ab3a1acec5f1e3faa7e8cf2f1e08d9b561cb41b845f69d5", size = 759523 },
+    { url = "https://files.pythonhosted.org/packages/2b/b2/e3234f59ba06559c6ff63c4e10baea10e5e7df868092bf9ab40e5b9c56b6/PyYAML-6.0.2-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:41e4e3953a79407c794916fa277a82531dd93aad34e29c2a514c2c0c5fe971cc", size = 726660 },
+    { url = "https://files.pythonhosted.org/packages/fe/0f/25911a9f080464c59fab9027482f822b86bf0608957a5fcc6eaac85aa515/PyYAML-6.0.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:68ccc6023a3400877818152ad9a1033e3db8625d899c72eacb5a668902e4d652", size = 751597 },
+    { url = "https://files.pythonhosted.org/packages/14/0d/e2c3b43bbce3cf6bd97c840b46088a3031085179e596d4929729d8d68270/PyYAML-6.0.2-cp313-cp313-win32.whl", hash = "sha256:bc2fa7c6b47d6bc618dd7fb02ef6fdedb1090ec036abab80d4681424b84c1183", size = 140527 },
+    { url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446 },
+]
+
 [[package]]
 name = "pyzmq"
 version = "26.3.0"
diff --git a/scripts/did/did_pa_multi_agg_coverage.py b/scripts/did/did_pa_multi_agg_coverage.py
index 01d95db..5878820 100644
--- a/scripts/did/did_pa_multi_agg_coverage.py
+++ b/scripts/did/did_pa_multi_agg_coverage.py
@@ -1,8 +1,14 @@
-from montecover.did import DIDMultiCoverageSimulation
 
-simulation = DIDMultiCoverageSimulation(
-    repetitions=5,
-    n_obs=500,
+from montecover.did.did_multi import DIDMultiCoverageSimulation
+
+# Create and run simulation with config file
+sim = DIDMultiCoverageSimulation(
+    config_file="scripts/did/multi_config.yml",
+    log_level="INFO",
+    log_file="logs/current_sim.log"
 )
-simulation.run_simulation()
-simulation.save_results(output_path="results/did/", file_prefix="did_pa_multi_coverage")
\ No newline at end of file
+sim.run_simulation()
+sim.save_results(output_path="results/did/", file_prefix="did_multi_run")
+
+# Save config file for reproducibility
+sim.save_config("results/did/multi_config.yml")
diff --git a/scripts/did/multi_config.yml b/scripts/did/multi_config.yml
new file mode 100644
index 0000000..87348f1
--- /dev/null
+++ b/scripts/did/multi_config.yml
@@ -0,0 +1,24 @@
+# Simulation parameters for DID Multi Coverage
+repetitions: 2
+max_runtime: 19800  # 5.5 hours in seconds
+random_seed: 42
+n_obs: 500  # Sample size
+
+# Parameter grid for simulation
+parameters:
+  DGP: [1, 4, 6]  # Different DGP specifications
+  score: 
+    - observational  # Standard DML score
+    - experimental   # Experimental score (no propensity estimation)
+  
+  in_sample_normalization: [true, false]
+  
+  # ML methods for outcome model
+  learner_g:
+    - ["Linear", "LinearRegression"]
+  
+  # ML methods for propensity score
+  learner_m:
+    - ["Linear", "LogisticRegression"]
+  
+  level: [0.95, 0.90]  # Confidence levels
\ No newline at end of file

From 2737ef6c30167a06c04fb79e72ba23d0f462bb62 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 16:10:40 +0100
Subject: [PATCH 12/60] seperate parameter configurations

---
 monte-cover/src/montecover/base.py          | 103 +++++++++-------
 monte-cover/src/montecover/did/did_multi.py | 123 +++++++++-----------
 2 files changed, 116 insertions(+), 110 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index cba4e8f..be7ef1f 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -6,7 +6,7 @@
 from abc import ABC, abstractmethod
 from datetime import datetime
 from itertools import product
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Optional
 
 import doubleml as dml
 import numpy as np
@@ -29,9 +29,10 @@ def __init__(
         self.config = self._load_config(config_file)
 
         # Apply parameters from config
-        self.repetitions = self.config.get("repetitions", 20)
-        self.max_runtime = self.config.get("max_runtime", 5.5 * 3600)
-        self.random_seed = self.config.get("random_seed", 42)
+        self.simulation_parameters = self.config.get("simulation_parameters", {})
+        self.repetitions = self.simulation_parameters.get("repetitions", 20)
+        self.max_runtime = self.simulation_parameters.get("max_runtime", 5.5 * 3600)
+        self.random_seed = self.simulation_parameters.get("random_seed", 42)
         self.suppress_warnings = suppress_warnings
 
         # Set up logging
@@ -55,8 +56,16 @@ def __init__(
         self.logger.info(f"Initialized simulation with random seed {self.random_seed}")
 
         # Let child classes process any specific parameters
+        self.dgp_parameters = self.config.get("dgp_parameters", {})
+        self.dml_parameters = self.config.get("dml_parameters", {})
+        self.confidence_parameters = self.config.get("confidence_parameters", {})
         self._process_config_parameters()
 
+    @abstractmethod
+    def _generate_dml_data(self, dgp_params: Dict[str, Any]) -> dml.DoubleMLData:
+        """Generate data for the DoubleML simulation."""
+        pass
+
     @abstractmethod
     def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
         """Run a single repetition with the given parameters."""
@@ -67,23 +76,19 @@ def summarize_results(self) -> Dict[str, Any]:
         """Summarize the simulation results."""
         pass
 
-    def get_parameters(self) -> Dict[str, List[Any]]:
-        """Get parameter grid from config."""
-        param_grid = self.config.get("parameters", {})
-        if not param_grid:
-            self.logger.warning("No parameter grid found in config. Using empty parameter set.")
-        return param_grid
-
     def run_simulation(self):
         """Run the full simulation."""
         self.start_time = time.time()
         self.logger.info("Starting simulation")
 
-        param_grid = self.get_parameters()
-        self.logger.info(f"Parameter grid: {param_grid}")
+        self.logger.info(f"DGP Parameters: {self.dgp_parameters}")
+        self.logger.info(f"DML Parameters: {self.dml_parameters}")
+        self.logger.info(f"Confidence Parameters: {self.confidence_parameters}")
 
         # Calculate total number of parameter combinations
-        total_combinations = np.prod([len(values) for values in param_grid.values()])
+        dgp_combinations = [len(v) for v in self.dgp_parameters.values()]
+        dml_combinations = [len(v) for v in self.dml_parameters.values()]
+        total_combinations = np.prod(dgp_combinations + dml_combinations)
         self.logger.info(f"Total parameter combinations: {total_combinations}")
 
         # Calculate expected total iterations
@@ -102,36 +107,46 @@ def run_simulation(self):
                 break
 
             param_combo = 0
-            keys = param_grid.keys()
-            for values in product(*param_grid.values()):
-                param_combo += 1
-                params = dict(zip(keys, values))
-                param_start_time = time.time()
-
-                # Log parameter combination
-                self.logger.debug(f"Rep {i_rep+1}, Combo {param_combo}/{total_combinations}: {params}")
-
-                try:
-                    repetition_results = self.run_single_rep(params)
-                    param_end_time = time.time()
-                    param_duration = param_end_time - param_start_time
-
-                    if repetition_results is not None:
-                        assert isinstance(repetition_results, dict), "The result must be a dictionary."
-                        # Process each dataframe in the result dictionary
-                        for result_name, repetition_result in repetition_results.items():
-                            assert isinstance(repetition_result, dict), "Each result must be a dictionary."
-                            repetition_result["repetition"] = i_rep
-
-                            # Initialize key in results dict if not exists
-                            if result_name not in self.results:
-                                self.results[result_name] = []
-                            self.results[result_name].append(repetition_result)
-
-                        self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s")
-                except Exception as e:
-                    self.logger.error(f"Error in repetition {i_rep+1}, parameters {params}: {str(e)}")
-                    self.logger.exception("Exception details:")
+            # loop through all
+            for dgp_param_values in product(*self.dgp_parameters.values()):
+                dgp_params = dict(zip(self.dgp_parameters.keys(), dgp_param_values))
+                dml_data = self._generate_dml_data(dgp_params)
+
+                for dml_param_values in product(*self.dml_parameters.values()):
+                    dml_params = dict(zip(self.dml_parameters.keys(), dml_param_values))
+
+                    param_combo += 1
+                    # Log parameter combination
+                    self.logger.debug(
+                        f"Rep {i_rep+1}, Combo {param_combo}/{total_combinations}: DGPs {dgp_params}, DML {dml_params}"
+                    )
+                    param_start_time = time.time()
+
+                    try:
+                        repetition_results = self.run_single_rep(dml_data, dml_params)
+                        param_end_time = time.time()
+                        param_duration = param_end_time - param_start_time
+
+                        if repetition_results is not None:
+                            assert isinstance(repetition_results, dict), "The result must be a dictionary."
+                            # Process each dataframe in the result dictionary
+                            for result_name, repetition_result in repetition_results.items():
+                                assert isinstance(repetition_result, dict), "Each result must be a dictionary."
+                                repetition_result["repetition"] = i_rep
+                                # add dgp parameters to the result
+                                repetition_result.update(dgp_params)
+
+                                # Initialize key in results dict if not exists
+                                if result_name not in self.results:
+                                    self.results[result_name] = []
+                                self.results[result_name].append(repetition_result)
+
+                            self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s")
+                    except Exception as e:
+                        self.logger.error(
+                            f"Error: repetition {i_rep+1}, DGP parameters {dgp_params}, DML parameters {dml_params}: {str(e)}"
+                        )
+                        self.logger.exception("Exception details:")
 
             rep_end_time = time.time()
             rep_duration = rep_end_time - rep_start_time
diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
index 68ce5f0..e7dfd56 100644
--- a/monte-cover/src/montecover/did/did_multi.py
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -4,6 +4,7 @@
 import numpy as np
 import pandas as pd
 from doubleml.did.datasets import make_did_CS2021
+from lightgbm import LGBMClassifier, LGBMRegressor
 from sklearn.linear_model import LinearRegression, LogisticRegression
 
 from montecover.base import BaseSimulation
@@ -34,32 +35,28 @@ def __init__(
 
     def _process_config_parameters(self):
         """Process simulation-specific parameters from config"""
-        # Extract n_obs from config or use default
-        self.n_obs = self.config.get("n_obs", 500)
-
         # Process ML models in parameter grid
-        if "parameters" in self.config:
-            param_grid = self.config["parameters"]
-
-            # Convert learner_g strings to actual objects
-            if "learner_g" in param_grid:
-                processed_learners_g = []
-                for learner in param_grid["learner_g"]:
-                    if isinstance(learner, list) and len(learner) == 2:
-                        if learner[0] == "Linear":
-                            processed_learners_g.append(("Linear", LinearRegression()))
-                        # Add more model types as needed
-                param_grid["learner_g"] = processed_learners_g
-
-            # Convert learner_m strings to actual objects
-            if "learner_m" in param_grid:
-                processed_learners_m = []
-                for learner in param_grid["learner_m"]:
-                    if isinstance(learner, list) and len(learner) == 2:
-                        if learner[0] == "Linear":
-                            processed_learners_m.append(("Linear", LogisticRegression()))
-                        # Add more model types as needed
-                param_grid["learner_m"] = processed_learners_m
+
+        assert "learners" in self.dml_parameters, "No learners specified in the config file"
+        for learner in self.dml_parameters["learners"]:
+            assert "ml_g" in learner, "No ml_g specified in the config file"
+            assert "ml_m" in learner, "No ml_m specified in the config file"
+
+            # Convert ml_g strings to actual objects
+            if learner["ml_g"][0] == "Linear":
+                learner["ml_g"] = ("Linear", LinearRegression())
+            elif learner["ml_g"][0] == "LGBM":
+                learner["ml_g"] = ("LGBM", LGBMRegressor(n_estimators=5, verbose=-1))
+            else:
+                raise ValueError(f"Unknown learner type: {learner['ml_g']}")
+
+            # Convert ml_m strings to actual objects
+            if learner["ml_m"][0] == "Linear":
+                learner["ml_m"] = ("Linear", LogisticRegression())
+            elif learner["ml_m"][0] == "LGBM":
+                learner["ml_m"] = ("LGBM", LGBMClassifier(n_estimators=5, verbose=-1))
+            else:
+                raise ValueError(f"Unknown learner type: {learner['ml_m']}")
 
     def _calculate_oracle_values(self):
         """Calculate oracle values for the simulation."""
@@ -84,19 +81,13 @@ def _calculate_oracle_values(self):
         ).values.astype("datetime64[M]")
         self.oracle_values["eventstudy"] = df_oracle.groupby("e")["ite"].mean()[1:]
 
-    def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
+    def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
         """Run a single repetition with the given parameters."""
         # Extract parameters
-        dgp_type = params["DGP"]
-        score = params["score"]
-        in_sample_normalization = params["in_sample_normalization"]
-        learner_g_name, ml_g = params["learner_g"]
-        learner_m_name, ml_m = params["learner_m"]
-        level = params["level"]
-
-        self.logger.debug(f"Running with DGP={dgp_type}, score={score}, level={level}")
-
-        dml_data = self._generate_data(dgp_type=dgp_type)
+        learner_g_name, ml_g = dml_params["learners"]["ml_g"]
+        learner_m_name, ml_m = dml_params["learners"]["ml_m"]
+        score = dml_params["score"]
+        in_sample_normalization = dml_params["in_sample_normalization"]
 
         # Model
         dml_model = dml.did.DoubleMLDIDMulti(
@@ -118,36 +109,36 @@ def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
             oracle_thetas[i] = self.oracle_values["detailed"][group_index & time_index]["ite"].iloc[0]
 
         result = dict()
-        result["detailed"] = self._compute_coverage(
-            thetas=dml_model.coef,
-            oracle_thetas=oracle_thetas,
-            confint=dml_model.confint(level=level),
-            joint_confint=dml_model.confint(level=level, joint=True),
-        )
-
-        for aggregation_method in ["group", "time", "eventstudy"]:
-            agg_obj = dml_model.aggregate(aggregation=aggregation_method)
-            agg_obj.aggregated_frameworks.bootstrap(n_rep_boot=2000)
-
-            result[aggregation_method] = self._compute_coverage(
-                thetas=agg_obj.aggregated_frameworks.thetas,
-                oracle_thetas=self.oracle_values[aggregation_method].values,
-                confint=agg_obj.aggregated_frameworks.confint(level=level),
-                joint_confint=agg_obj.aggregated_frameworks.confint(level=level, joint=True),
+        for level in self.confidence_parameters["level"]:
+            result["detailed"] = self._compute_coverage(
+                thetas=dml_model.coef,
+                oracle_thetas=oracle_thetas,
+                confint=dml_model.confint(level=level),
+                joint_confint=dml_model.confint(level=level, joint=True),
             )
 
-        # add parameters to the result
-        for result_dict in result.values():
-            result_dict.update(
-                {
-                    "Learner g": learner_g_name,
-                    "Learner m": learner_m_name,
-                    "Score": score,
-                    "In-sample-norm.": in_sample_normalization,
-                    "DGP": dgp_type,
-                    "level": level,
-                }
-            )
+            for aggregation_method in ["group", "time", "eventstudy"]:
+                agg_obj = dml_model.aggregate(aggregation=aggregation_method)
+                agg_obj.aggregated_frameworks.bootstrap(n_rep_boot=2000)
+
+                result[aggregation_method] = self._compute_coverage(
+                    thetas=agg_obj.aggregated_frameworks.thetas,
+                    oracle_thetas=self.oracle_values[aggregation_method].values,
+                    confint=agg_obj.aggregated_frameworks.confint(level=level),
+                    joint_confint=agg_obj.aggregated_frameworks.confint(level=level, joint=True),
+                )
+
+            # add parameters to the result
+            for result_dict in result.values():
+                result_dict.update(
+                    {
+                        "Learner g": learner_g_name,
+                        "Learner m": learner_m_name,
+                        "Score": score,
+                        "In-sample-norm.": in_sample_normalization,
+                        "level": level,
+                    }
+                )
 
         return result
 
@@ -172,9 +163,9 @@ def summarize_results(self):
 
         return result_summary
 
-    def _generate_data(self, dgp_type: int) -> dml.data.DoubleMLPanelData:
+    def _generate_dml_data(self, dgp_params) -> dml.data.DoubleMLPanelData:
         """Generate data for the simulation."""
-        data = make_did_CS2021(n_obs=self.n_obs, dgp_type=dgp_type)
+        data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"])
         dml_data = dml.data.DoubleMLPanelData(
             data,
             y_col="y",

From 2b9bdfca747f434d339d6eec043224afed0ca972 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 16:38:36 +0100
Subject: [PATCH 13/60] run multi simulation

---
 results/did/did_multi_detailed.csv       |  13 ++
 results/did/did_multi_eventstudy.csv     |  13 ++
 results/did/did_multi_group.csv          |  13 ++
 results/did/did_multi_metadata.csv       |   2 +
 results/did/did_multi_time.csv           |  13 ++
 results/did/multi_config.yml             |  50 +++++
 scripts/did/did_pa_multi_agg_coverage.py |  14 --
 scripts/did/did_pa_multi_coverage.py     | 226 ++---------------------
 scripts/did/multi_config.yml             |  43 ++---
 9 files changed, 137 insertions(+), 250 deletions(-)
 create mode 100644 results/did/did_multi_detailed.csv
 create mode 100644 results/did/did_multi_eventstudy.csv
 create mode 100644 results/did/did_multi_group.csv
 create mode 100644 results/did/did_multi_metadata.csv
 create mode 100644 results/did/did_multi_time.csv
 create mode 100644 results/did/multi_config.yml
 delete mode 100644 scripts/did/did_pa_multi_agg_coverage.py

diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
new file mode 100644
index 0000000..ddbb273
--- /dev/null
+++ b/results/did/did_multi_detailed.csv
@@ -0,0 +1,13 @@
+Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
+Linear,Linear,experimental,False,1,0.9,0.8770833333333333,0.5957148258014461,0.15382974604923927,0.82,0.9277609206218338,200
+Linear,Linear,experimental,False,4,0.9,0.6683333333333333,1.9698836561519315,0.8284907405553992,0.525,2.863183256412114,200
+Linear,Linear,experimental,False,6,0.9,0.89375,2.012015821768593,0.4972286199594851,0.9,2.910046927597859,200
+Linear,Linear,experimental,True,1,0.9,0.8745833333333333,0.5956334541132615,0.1540676164908103,0.82,0.929071986266196,200
+Linear,Linear,experimental,True,4,0.9,0.6695833333333333,1.9690878328880732,0.8280711666335188,0.515,2.8592795655154974,200
+Linear,Linear,experimental,True,6,0.9,0.8954166666666667,2.012277261422281,0.4972240370172027,0.905,2.910046165626694,200
+Linear,Linear,observational,False,1,0.9,0.89625,0.686900650227242,0.16556887731250863,0.905,1.0684577495769967,200
+Linear,Linear,observational,False,4,0.9,0.7866666666666667,3.124729480506762,0.9502826142586591,0.735,4.394633218514321,200
+Linear,Linear,observational,False,6,0.9,0.8970833333333332,2.773055872422081,0.7355132665655921,0.915,3.9680250024995116,200
+Linear,Linear,observational,True,1,0.9,0.88875,0.6660387052074566,0.16288234355298709,0.875,1.03599720010251,200
+Linear,Linear,observational,True,4,0.9,0.7770833333333332,2.832512839406202,0.8935845118739927,0.72,4.034877974695546,200
+Linear,Linear,observational,True,6,0.9,0.8975,2.5644621837469375,0.675326608841152,0.9,3.688249907793509,200
diff --git a/results/did/did_multi_eventstudy.csv b/results/did/did_multi_eventstudy.csv
new file mode 100644
index 0000000..5486774
--- /dev/null
+++ b/results/did/did_multi_eventstudy.csv
@@ -0,0 +1,13 @@
+Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
+Linear,Linear,experimental,False,1,0.9,0.8833333333333333,0.42565718743306286,0.10936767904635211,0.835,0.6077012485542163,200
+Linear,Linear,experimental,False,4,0.9,0.6125,1.9569891887347262,0.9196943825351817,0.545,2.5358161709127596,200
+Linear,Linear,experimental,False,6,0.9,0.8975,2.0153850665398654,0.5111684848078778,0.88,2.5922545970785786,200
+Linear,Linear,experimental,True,1,0.9,0.8791666666666668,0.4255897490005149,0.10926516710603056,0.845,0.6066297468359033,200
+Linear,Linear,experimental,True,4,0.9,0.605,1.9564893215757844,0.9195779385934096,0.55,2.5307678617965537,200
+Linear,Linear,experimental,True,6,0.9,0.8941666666666667,2.015463986254987,0.5113312788664856,0.89,2.5944084708379807,200
+Linear,Linear,observational,False,1,0.9,0.9083333333333333,0.4882437838128583,0.11428563471509198,0.9,0.6956288268269425,200
+Linear,Linear,observational,False,4,0.9,0.7691666666666667,3.3932130254913604,1.0549489318868936,0.755,4.223665366280769,200
+Linear,Linear,observational,False,6,0.9,0.8958333333333333,2.8512971267032157,0.7733310576205062,0.895,3.607707492218858,200
+Linear,Linear,observational,True,1,0.9,0.8975,0.4745126476352591,0.11192315736615333,0.895,0.6757224816274575,200
+Linear,Linear,observational,True,4,0.9,0.7566666666666667,3.005791111628938,0.9858031123253609,0.71,3.794083607223553,200
+Linear,Linear,observational,True,6,0.9,0.8933333333333333,2.6626861416696204,0.7105550768719945,0.895,3.3880858319786715,200
diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
new file mode 100644
index 0000000..a4dbdc4
--- /dev/null
+++ b/results/did/did_multi_group.csv
@@ -0,0 +1,13 @@
+Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
+Linear,Linear,experimental,False,1,0.9,0.8666666666666667,0.533698038133595,0.14197945807093054,0.85,0.6861047473750901,200
+Linear,Linear,experimental,False,4,0.9,0.66,2.1748653096663464,0.9342553462922274,0.525,2.745378335374161,200
+Linear,Linear,experimental,False,6,0.9,0.8883333333333333,2.217594064950379,0.5525144231877022,0.875,2.791344195057897,200
+Linear,Linear,experimental,True,1,0.9,0.87,0.5335807495502426,0.14168060865880763,0.865,0.6849043629713572,200
+Linear,Linear,experimental,True,4,0.9,0.6633333333333333,2.173005344301668,0.9333928409584715,0.51,2.7419250872101033,200
+Linear,Linear,experimental,True,6,0.9,0.8933333333333333,2.2188654092146014,0.5513512630008591,0.85,2.790603998972855,200
+Linear,Linear,observational,False,1,0.9,0.8966666666666667,0.6108816472728611,0.145895094484495,0.87,0.783552984172717,200
+Linear,Linear,observational,False,4,0.9,0.78,3.5092375556264455,1.0649254128150685,0.73,4.316704955507763,200
+Linear,Linear,observational,False,6,0.9,0.895,2.9637734181525097,0.7818480925391569,0.9,3.6693459962202515,200
+Linear,Linear,observational,True,1,0.9,0.895,0.5920636535297172,0.1408811699533362,0.885,0.7597493231438954,200
+Linear,Linear,observational,True,4,0.9,0.7633333333333333,3.124012529892269,0.9973634763359689,0.72,3.880323357977367,200
+Linear,Linear,observational,True,6,0.9,0.8966666666666667,2.823741863032981,0.7474950667730065,0.88,3.5108185024043683,200
diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
new file mode 100644
index 0000000..8f0a64a
--- /dev/null
+++ b/results/did/did_multi_metadata.csv
@@ -0,0 +1,2 @@
+DoubleML Version,Script,Date,Total Runtime (seconds),Python Version,Config File
+0.10.dev0,DIDMultiCoverageSimulation,2025-03-19 16:37:01,1605.1626930236816,3.12.9,scripts/did/multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
new file mode 100644
index 0000000..dd6f277
--- /dev/null
+++ b/results/did/did_multi_time.csv
@@ -0,0 +1,13 @@
+Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
+Linear,Linear,experimental,False,1,0.9,0.85,0.49360461245245374,0.13317137989658068,0.805,0.6319610870324371,200
+Linear,Linear,experimental,False,4,0.9,0.495,1.943482564791512,1.0099967068690494,0.475,2.237010089005512,200
+Linear,Linear,experimental,False,6,0.9,0.885,1.973586708219856,0.5137628285676683,0.875,2.269948492397086,200
+Linear,Linear,experimental,True,1,0.9,0.8533333333333333,0.4936441210979332,0.13386449414919313,0.815,0.6316313581946184,200
+Linear,Linear,experimental,True,4,0.9,0.495,1.9419691602716298,1.0073536527153972,0.485,2.233138294309388,200
+Linear,Linear,experimental,True,6,0.9,0.8916666666666667,1.9743669935047528,0.5111362751423758,0.895,2.2727934396360774,200
+Linear,Linear,observational,False,1,0.9,0.905,0.5957890718380163,0.14322442977409994,0.905,0.7621516555071253,200
+Linear,Linear,observational,False,4,0.9,0.7166666666666667,3.587198674078982,1.1518798523193952,0.695,4.005822809393348,200
+Linear,Linear,observational,False,6,0.9,0.8966666666666667,2.8422258436545302,0.7656607981855843,0.9,3.235109337869692,200
+Linear,Linear,observational,True,1,0.9,0.8966666666666667,0.5736643332685405,0.13819068965761655,0.89,0.7334447199193829,200
+Linear,Linear,observational,True,4,0.9,0.7016666666666667,3.135225381339414,1.0623341589035538,0.66,3.525109938704791,200
+Linear,Linear,observational,True,6,0.9,0.8816666666666667,2.6295968848181595,0.7121484510464997,0.87,3.011021831651718,200
diff --git a/results/did/multi_config.yml b/results/did/multi_config.yml
new file mode 100644
index 0000000..b53271c
--- /dev/null
+++ b/results/did/multi_config.yml
@@ -0,0 +1,50 @@
+confidence_parameters:
+  level:
+  - 0.95
+  - 0.9
+dgp_parameters:
+  DGP:
+  - 1
+  - 4
+  - 6
+  n_obs:
+  - 500
+dml_parameters:
+  in_sample_normalization:
+  - true
+  - false
+  learners:
+  - ml_g: !!python/tuple
+    - Linear
+    - !!python/object:sklearn.linear_model._base.LinearRegression
+      _sklearn_version: 1.5.2
+      copy_X: true
+      fit_intercept: true
+      n_jobs: null
+      positive: false
+    ml_m: !!python/tuple
+    - Linear
+    - !!python/object:sklearn.linear_model._logistic.LogisticRegression
+      C: 1.0
+      _sklearn_version: 1.5.2
+      class_weight: null
+      dual: false
+      fit_intercept: true
+      intercept_scaling: 1
+      l1_ratio: null
+      max_iter: 100
+      multi_class: deprecated
+      n_jobs: null
+      penalty: l2
+      random_state: null
+      solver: lbfgs
+      tol: 0.0001
+      verbose: 0
+      warm_start: false
+  score:
+  - observational
+  - experimental
+simulation_parameters:
+  max_runtime: 19800
+  random_seed: 42
+  repetitions: 200
diff --git a/scripts/did/did_pa_multi_agg_coverage.py b/scripts/did/did_pa_multi_agg_coverage.py
deleted file mode 100644
index 5878820..0000000
--- a/scripts/did/did_pa_multi_agg_coverage.py
+++ /dev/null
@@ -1,14 +0,0 @@
-
-from montecover.did.did_multi import DIDMultiCoverageSimulation
-
-# Create and run simulation with config file
-sim = DIDMultiCoverageSimulation(
-    config_file="scripts/did/multi_config.yml",
-    log_level="INFO",
-    log_file="logs/current_sim.log"
-)
-sim.run_simulation()
-sim.save_results(output_path="results/did/", file_prefix="did_multi_run")
-
-# Save config file for reproducibility
-sim.save_config("results/did/multi_config.yml")
diff --git a/scripts/did/did_pa_multi_coverage.py b/scripts/did/did_pa_multi_coverage.py
index 664bbf1..22251f2 100644
--- a/scripts/did/did_pa_multi_coverage.py
+++ b/scripts/did/did_pa_multi_coverage.py
@@ -1,218 +1,14 @@
-import numpy as np
-import pandas as pd
-from datetime import datetime
-import time
-import sys
-import warnings
 
-from sklearn.linear_model import LinearRegression, LogisticRegression
-from lightgbm import LGBMRegressor, LGBMClassifier
+from montecover.did.did_multi import DIDMultiCoverageSimulation
 
-import doubleml as dml
-from doubleml.did.datasets import make_did_CS2021
+# Create and run simulation with config file
+sim = DIDMultiCoverageSimulation(
+    config_file="scripts/did/multi_config.yml",
+    log_level="INFO",
+    log_file="logs/current_sim.log"
+)
+sim.run_simulation()
+sim.save_results(output_path="results/did/", file_prefix="did_multi")
 
-# Suppress warnings
-warnings.simplefilter(action='ignore', category=UserWarning)
-
-# Number of repetitions
-n_rep = 20
-max_runtime = 5.5 * 3600  # 5.5 hours in seconds
-
-# Oracle values
-df_oracle = make_did_CS2021(n_obs=int(1e+6), dgp_type=1)  # does not depend on the DGP type
-df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"]
-df_oracle_thetas = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
-print("ATTs:")
-print(df_oracle_thetas)
-
-df_oracle_post_treatment = df_oracle[df_oracle["t"] >= df_oracle["d"]]
-# Group aggregation
-df_oracle_agg_group = df_oracle_post_treatment.groupby("d")["ite"].mean()
-print("Group aggregated ATTs:")
-print(df_oracle_agg_group)
-
-# Time aggregation
-df_oracle_agg_time = df_oracle_post_treatment.groupby("t")["ite"].mean()
-print("Time aggregated ATTs:")
-print(df_oracle_agg_time)
-
-# Eventstudy aggregation
-df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype("datetime64[M]") - \
-    pd.to_datetime(df_oracle["d"]).values.astype("datetime64[M]")
-df_oracle_agg_eventstudy = df_oracle.groupby("e")["ite"].mean()
-print("Event Study aggregated ATTs:")
-print(df_oracle_agg_eventstudy)
-
-# DGP pars
-n_obs = 500
-dgp_types = [1, 4, 6]
-
-# set up hyperparameters
-hyperparam_dict = {
-    "DGP": dgp_types,
-    "score": ["observational", "experimental"],
-    "in sample normalization": [True, False],
-    "learner_g": [("Linear", LinearRegression()),],
-    "learner_m": [("Linear", LogisticRegression()),],
-    "level": [0.95, 0.90]
-}
-
-# set up the results dataframe
-df_results_detailed = pd.DataFrame()
-df_results_agg = pd.DataFrame()
-
-# start simulation
-np.random.seed(42)
-start_time = time.time()
-
-for i_rep in range(n_rep):
-    print(f"Repetition: {i_rep + 1}/{n_rep}", end="\r")
-
-    # Check the elapsed time
-    elapsed_time = time.time() - start_time
-    if elapsed_time > max_runtime:
-        print("Maximum runtime exceeded. Stopping the simulation.")
-        break
-
-    for i_dgp, dgp_type in enumerate(dgp_types):
-        # define the DoubleML data object
-        data = make_did_CS2021(n_obs=n_obs, dgp_type=dgp_type)
-        obj_dml_data = dml.data.DoubleMLPanelData(
-            data,
-            y_col="y",
-            d_cols="d",
-            id_col="id",
-            t_col="t",
-            x_cols=["Z1", "Z2", "Z3", "Z4"],
-        )
-
-        for learner_g_idx, (learner_g_name, ml_g) in enumerate(hyperparam_dict["learner_g"]):
-            for learner_m_idx, (learner_m_name, ml_m) in enumerate(hyperparam_dict["learner_m"]):
-                for score in hyperparam_dict["score"]:
-                    for in_sample_normalization in hyperparam_dict["in sample normalization"]:
-                        if score == "experimental":
-                            dml_DiD = dml.did.DoubleMLDIDMulti(
-                                obj_dml_data=obj_dml_data,
-                                ml_g=ml_g,
-                                ml_m=None,
-                                gt_combinations="standard",
-                                score=score,
-                                in_sample_normalization=in_sample_normalization)
-                        else:
-                            assert score == "observational"
-                            dml_DiD = dml.did.DoubleMLDIDMulti(
-                                obj_dml_data=obj_dml_data,
-                                ml_g=ml_g,
-                                ml_m=ml_m,
-                                gt_combinations="standard",
-                                score=score,
-                                in_sample_normalization=in_sample_normalization)
-                        dml_DiD.fit(n_jobs_cv=5)
-
-                        # oracle values
-                        theta = np.full_like(dml_DiD.coef, np.nan)
-                        for i, (g, _, t) in enumerate(dml_DiD.gt_combinations):
-                            group_index = df_oracle_thetas["d"] == g
-                            time_index = df_oracle_thetas["t"] == t
-                            theta[i] = df_oracle_thetas[group_index & time_index]["ite"].iloc[0]
-
-                        for level_idx, level in enumerate(hyperparam_dict["level"]):
-                            confint = dml_DiD.confint(level=level)
-                            coverage = np.mean((confint.iloc[:, 0] < theta) & (theta < confint.iloc[:, 1]))
-                            ci_length = np.mean(confint.iloc[:, 1] - confint.iloc[:, 0])
-                            bias = np.mean(abs(dml_DiD.coef - theta))
-
-                            dml_DiD.bootstrap(n_rep_boot=2000)
-                            confint_uniform = dml_DiD.confint(level=level, joint=True)
-
-                            coverage_uniform = all((confint_uniform.iloc[:, 0] < theta) & (theta < confint_uniform.iloc[:, 1]))
-                            ci_length_uniform = np.mean(confint_uniform.iloc[:, 1] - confint_uniform.iloc[:, 0])
-
-                            df_results_detailed = pd.concat(
-                                (df_results_detailed,
-                                 pd.DataFrame({
-                                    "Coverage": coverage,
-                                    "CI Length": ci_length,
-                                    "Bias": bias,
-                                    "Uniform Coverage": coverage_uniform,
-                                    "Uniform CI Length": ci_length_uniform,
-                                    "Learner g": learner_g_name,
-                                    "Learner m": learner_m_name,
-                                    "Score": score,
-                                    "In-sample-norm.": in_sample_normalization,
-                                    "DGP": dgp_type,
-                                    "level": level,
-                                    "repetition": i_rep}, index=[0])),
-                                ignore_index=True)
-                            
-                            # group aggregation
-                            group_agg = dml_DiD.aggregate(aggregation="group")
-                            group_confint = group_agg.aggregated_frameworks.confint(level=level)
-                            group_coverage = np.mean((group_confint.iloc[:, 0] < df_oracle_agg_group.values) & (df_oracle_agg_group.values < group_confint.iloc[:, 1]))
-                            group_ci_length = np.mean(group_confint.iloc[:, 1] - group_confint.iloc[:, 0])
-                            group_bias = np.mean(abs(group_agg.aggregated_frameworks.thetas - df_oracle_agg_group.values))
-
-                            group_agg.aggregated_frameworks.bootstrap(n_rep_boot=2000)
-                            group_confint_uniform = group_agg.aggregated_frameworks.confint(level=level, joint=True)
-                            group_coverage_uniform = all((group_confint_uniform.iloc[:, 0] < df_oracle_agg_group.values) & (df_oracle_agg_group.values < group_confint_uniform.iloc[:, 1]))
-                            group_ci_length_uniform = np.mean(group_confint_uniform.iloc[:, 1] - group_confint_uniform.iloc[:, 0])
-
-                            df_results_agg = pd.concat(
-                                (df_results_agg,
-                                 pd.DataFrame({
-                                    "Coverage": group_coverage,
-                                    "CI Length": group_ci_length,
-                                    "Bias": group_bias,
-                                    "Uniform Coverage": group_coverage_uniform,
-                                    "Uniform CI Length": group_ci_length_uniform,
-                                    "Learner g": learner_g_name,
-                                    "Learner m": learner_m_name,
-                                    "Score": score,
-                                    "In-sample-norm.": in_sample_normalization,
-                                    "DGP": dgp_type,
-                                    "level": level,
-                                    "repetition": i_rep}, index=[0]),
-                                ),
-                                ignore_index=True)
-
-
-df_results = df_results_detailed.groupby(
-    ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]).agg(
-        {"Coverage": "mean",
-         "CI Length": "mean",
-         "Bias": "mean",
-         "Uniform Coverage": "mean",
-         "Uniform CI Length": "mean",
-         "repetition": "count"}
-    ).reset_index()
-print(df_results)
-
-df_results_agg = df_results_agg.groupby(
-    ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]).agg(
-        {"Coverage": "mean",
-         "CI Length": "mean",
-         "Bias": "mean",
-         "Uniform Coverage": "mean",
-         "Uniform CI Length": "mean",
-         "repetition": "count"}
-    ).reset_index()
-print(df_results_agg)
-
-end_time = time.time()
-total_runtime = end_time - start_time
-
-# save results
-script_name = "did_pa_multi_coverage.py"
-path = "results/did/did_pa_multi_coverage"
-
-metadata = pd.DataFrame({
-    'DoubleML Version': [dml.__version__],
-    'Script': [script_name],
-    'Date': [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
-    'Total Runtime (seconds)': [total_runtime],
-    'Python Version': [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"],
-})
-print(metadata)
-
-df_results.to_csv(f"{path}.csv", index=False)
-metadata.to_csv(f"{path}_metadata.csv", index=False)
+# Save config file for reproducibility
+sim.save_config("results/did/multi_config.yml")
diff --git a/scripts/did/multi_config.yml b/scripts/did/multi_config.yml
index 87348f1..de4d854 100644
--- a/scripts/did/multi_config.yml
+++ b/scripts/did/multi_config.yml
@@ -1,24 +1,25 @@
 # Simulation parameters for DID Multi Coverage
-repetitions: 2
-max_runtime: 19800  # 5.5 hours in seconds
-random_seed: 42
-n_obs: 500  # Sample size
 
-# Parameter grid for simulation
-parameters:
-  DGP: [1, 4, 6]  # Different DGP specifications
-  score: 
-    - observational  # Standard DML score
-    - experimental   # Experimental score (no propensity estimation)
-  
+simulation_parameters:
+  repetitions: 200
+  max_runtime: 19800 # 5.5 hours in seconds
+  random_seed: 42
+
+dgp_parameters:
+  DGP: [1, 4, 6] # Different DGP specifications
+  n_obs: [500] # Sample size for each simulation (has to be a list)
+
+dml_parameters:
+  # ML methods for ml_g and ml_m
+  learners:
+    - ml_g: ["Linear"]
+      ml_m: ["Linear"]
+
+  score:
+    - observational # Standard DML score
+    - experimental # Experimental score (no propensity estimation)
+
   in_sample_normalization: [true, false]
-  
-  # ML methods for outcome model
-  learner_g:
-    - ["Linear", "LinearRegression"]
-  
-  # ML methods for propensity score
-  learner_m:
-    - ["Linear", "LogisticRegression"]
-  
-  level: [0.95, 0.90]  # Confidence levels
\ No newline at end of file
+
+confidence_parameters:
+  level: [0.95, 0.90] # Confidence levels

From c8e229696605cc2d640cdf29c516417dbb46ccbf Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 16:50:01 +0100
Subject: [PATCH 14/60] update metadata

---
 monte-cover/src/montecover/base.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index be7ef1f..c374a9f 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -172,8 +172,8 @@ def save_results(self, output_path: str = "results", file_prefix: str = ""):
             {
                 "DoubleML Version": [dml.__version__],
                 "Script": [self.__class__.__name__],
-                "Date": [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
-                "Total Runtime (seconds)": [self.total_runtime],
+                "Date": [datetime.now().strftime("%Y-%m-%d %H:%M")],
+                "Total Runtime (minutes)": [self.total_runtime / 60],
                 "Python Version": [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"],
                 "Config File": [self.config_file],
             }

From 26002af2cc82327a1e10ac3f1527f060ebd316ec Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 17:56:30 +0100
Subject: [PATCH 15/60] update sim with different levels

---
 monte-cover/src/montecover/base.py          | 12 +++++++-----
 monte-cover/src/montecover/did/did_multi.py | 18 +++++++++++++-----
 results/did/did_multi_detailed.csv          | 12 ++++++++++++
 results/did/did_multi_eventstudy.csv        | 12 ++++++++++++
 results/did/did_multi_group.csv             | 12 ++++++++++++
 results/did/did_multi_metadata.csv          |  4 ++--
 results/did/did_multi_time.csv              | 12 ++++++++++++
 7 files changed, 70 insertions(+), 12 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index c374a9f..6eccc73 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -131,15 +131,17 @@ def run_simulation(self):
                             assert isinstance(repetition_results, dict), "The result must be a dictionary."
                             # Process each dataframe in the result dictionary
                             for result_name, repetition_result in repetition_results.items():
-                                assert isinstance(repetition_result, dict), "Each result must be a dictionary."
-                                repetition_result["repetition"] = i_rep
-                                # add dgp parameters to the result
-                                repetition_result.update(dgp_params)
+                                assert isinstance(repetition_result, list), "Each repetition_result must be a list."
+                                for res in repetition_result:
+                                    assert isinstance(res, dict), "Each res must be a dictionary."
+                                    res["repetition"] = i_rep
+                                    # add dgp parameters to the result
+                                    res.update(dgp_params)
 
                                 # Initialize key in results dict if not exists
                                 if result_name not in self.results:
                                     self.results[result_name] = []
-                                self.results[result_name].append(repetition_result)
+                                self.results[result_name].extend(repetition_result)
 
                             self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s")
                     except Exception as e:
diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
index e7dfd56..4eb6af0 100644
--- a/monte-cover/src/montecover/did/did_multi.py
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -108,9 +108,15 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
             time_index = self.oracle_values["detailed"]["t"] == t
             oracle_thetas[i] = self.oracle_values["detailed"][group_index & time_index]["ite"].iloc[0]
 
-        result = dict()
+        result = {
+            "detailed": [],
+            "group": [],
+            "time": [],
+            "eventstudy": [],
+        }
         for level in self.confidence_parameters["level"]:
-            result["detailed"] = self._compute_coverage(
+            level_result = dict()
+            level_result["detailed"] = self._compute_coverage(
                 thetas=dml_model.coef,
                 oracle_thetas=oracle_thetas,
                 confint=dml_model.confint(level=level),
@@ -121,7 +127,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
                 agg_obj = dml_model.aggregate(aggregation=aggregation_method)
                 agg_obj.aggregated_frameworks.bootstrap(n_rep_boot=2000)
 
-                result[aggregation_method] = self._compute_coverage(
+                level_result[aggregation_method] = self._compute_coverage(
                     thetas=agg_obj.aggregated_frameworks.thetas,
                     oracle_thetas=self.oracle_values[aggregation_method].values,
                     confint=agg_obj.aggregated_frameworks.confint(level=level),
@@ -129,8 +135,8 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
                 )
 
             # add parameters to the result
-            for result_dict in result.values():
-                result_dict.update(
+            for res in level_result.values():
+                res.update(
                     {
                         "Learner g": learner_g_name,
                         "Learner m": learner_m_name,
@@ -139,6 +145,8 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
                         "level": level,
                     }
                 )
+            for key, res in level_result.items():
+                result[key].append(res)
 
         return result
 
diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
index ddbb273..f37be82 100644
--- a/results/did/did_multi_detailed.csv
+++ b/results/did/did_multi_detailed.csv
@@ -1,13 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
 Linear,Linear,experimental,False,1,0.9,0.8770833333333333,0.5957148258014461,0.15382974604923927,0.82,0.9277609206218338,200
+Linear,Linear,experimental,False,1,0.95,0.9329166666666667,0.709837996826105,0.15382974604923927,0.9,1.0183441020334203,200
 Linear,Linear,experimental,False,4,0.9,0.6683333333333333,1.9698836561519315,0.8284907405553992,0.525,2.863183256412114,200
+Linear,Linear,experimental,False,4,0.95,0.7670833333333333,2.347261152317584,0.8284907405553992,0.64,3.1905243140809945,200
 Linear,Linear,experimental,False,6,0.9,0.89375,2.012015821768593,0.4972286199594851,0.9,2.910046927597859,200
+Linear,Linear,experimental,False,6,0.95,0.9441666666666667,2.3974647241407983,0.4972286199594851,0.935,3.246758702976773,200
 Linear,Linear,experimental,True,1,0.9,0.8745833333333333,0.5956334541132615,0.1540676164908103,0.82,0.929071986266196,200
+Linear,Linear,experimental,True,1,0.95,0.9333333333333332,0.7097410364792451,0.1540676164908103,0.895,1.0214045871405517,200
 Linear,Linear,experimental,True,4,0.9,0.6695833333333333,1.9690878328880732,0.8280711666335188,0.515,2.8592795655154974,200
+Linear,Linear,experimental,True,4,0.95,0.7670833333333333,2.346312870409903,0.8280711666335188,0.645,3.184749486936283,200
 Linear,Linear,experimental,True,6,0.9,0.8954166666666667,2.012277261422281,0.4972240370172027,0.905,2.910046165626694,200
+Linear,Linear,experimental,True,6,0.95,0.9433333333333332,2.3977762487025966,0.4972240370172027,0.93,3.2443247059312212,200
 Linear,Linear,observational,False,1,0.9,0.89625,0.686900650227242,0.16556887731250863,0.905,1.0684577495769967,200
+Linear,Linear,observational,False,1,0.95,0.9441666666666667,0.8184926083044464,0.16556887731250863,0.93,1.1743444720583638,200
 Linear,Linear,observational,False,4,0.9,0.7866666666666667,3.124729480506762,0.9502826142586591,0.735,4.394633218514321,200
+Linear,Linear,observational,False,4,0.95,0.8541666666666667,3.7233448270862413,0.9502826142586591,0.8,4.923945197030173,200
 Linear,Linear,observational,False,6,0.9,0.8970833333333332,2.773055872422081,0.7355132665655921,0.915,3.9680250024995116,200
+Linear,Linear,observational,False,6,0.95,0.9541666666666667,3.304299876906268,0.7355132665655921,0.95,4.437925510709768,200
 Linear,Linear,observational,True,1,0.9,0.88875,0.6660387052074566,0.16288234355298709,0.875,1.03599720010251,200
+Linear,Linear,observational,True,1,0.95,0.9408333333333333,0.7936340675709367,0.16288234355298709,0.925,1.1385204090656658,200
 Linear,Linear,observational,True,4,0.9,0.7770833333333332,2.832512839406202,0.8935845118739927,0.72,4.034877974695546,200
+Linear,Linear,observational,True,4,0.95,0.8545833333333333,3.375147222839926,0.8935845118739927,0.78,4.5253801160151035,200
 Linear,Linear,observational,True,6,0.9,0.8975,2.5644621837469375,0.675326608841152,0.9,3.688249907793509,200
+Linear,Linear,observational,True,6,0.95,0.9470833333333333,3.055745166318817,0.675326608841152,0.94,4.115336505261702,200
diff --git a/results/did/did_multi_eventstudy.csv b/results/did/did_multi_eventstudy.csv
index 5486774..5d8b92f 100644
--- a/results/did/did_multi_eventstudy.csv
+++ b/results/did/did_multi_eventstudy.csv
@@ -1,13 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
 Linear,Linear,experimental,False,1,0.9,0.8833333333333333,0.42565718743306286,0.10936767904635211,0.835,0.6077012485542163,200
+Linear,Linear,experimental,False,1,0.95,0.9358333333333333,0.507201822374698,0.10936767904635211,0.905,0.6747323911476379,200
 Linear,Linear,experimental,False,4,0.9,0.6125,1.9569891887347262,0.9196943825351817,0.545,2.5358161709127596,200
+Linear,Linear,experimental,False,4,0.95,0.7141666666666667,2.3318964467149415,0.9196943825351817,0.635,2.8827181581319787,200
 Linear,Linear,experimental,False,6,0.9,0.8975,2.0153850665398654,0.5111684848078778,0.88,2.5922545970785786,200
+Linear,Linear,experimental,False,6,0.95,0.9441666666666667,2.40147942690741,0.5111684848078778,0.94,2.9541718057018187,200
 Linear,Linear,experimental,True,1,0.9,0.8791666666666668,0.4255897490005149,0.10926516710603056,0.845,0.6066297468359033,200
+Linear,Linear,experimental,True,1,0.95,0.9358333333333333,0.5071214645259496,0.10926516710603056,0.915,0.6750610192677344,200
 Linear,Linear,experimental,True,4,0.9,0.605,1.9564893215757844,0.9195779385934096,0.55,2.5307678617965537,200
+Linear,Linear,experimental,True,4,0.95,0.7158333333333333,2.3313008182574744,0.9195779385934096,0.65,2.872282262053714,200
 Linear,Linear,experimental,True,6,0.9,0.8941666666666667,2.015463986254987,0.5113312788664856,0.89,2.5944084708379807,200
+Linear,Linear,experimental,True,6,0.95,0.9466666666666668,2.401573465548158,0.5113312788664856,0.93,2.9505612073229464,200
 Linear,Linear,observational,False,1,0.9,0.9083333333333333,0.4882437838128583,0.11428563471509198,0.9,0.6956288268269425,200
+Linear,Linear,observational,False,1,0.95,0.9533333333333333,0.5817783517444832,0.11428563471509198,0.945,0.7729973931119418,200
 Linear,Linear,observational,False,4,0.9,0.7691666666666667,3.3932130254913604,1.0549489318868936,0.755,4.223665366280769,200
+Linear,Linear,observational,False,4,0.95,0.8408333333333333,4.043262703053558,1.0549489318868936,0.81,4.8262642311066015,200
 Linear,Linear,observational,False,6,0.9,0.8958333333333333,2.8512971267032157,0.7733310576205062,0.895,3.607707492218858,200
+Linear,Linear,observational,False,6,0.95,0.955,3.397530081699918,0.7733310576205062,0.955,4.122679418390554,200
 Linear,Linear,observational,True,1,0.9,0.8975,0.4745126476352591,0.11192315736615333,0.895,0.6757224816274575,200
+Linear,Linear,observational,True,1,0.95,0.945,0.5654166938231102,0.11192315736615333,0.925,0.7517329711375669,200
 Linear,Linear,observational,True,4,0.9,0.7566666666666667,3.005791111628938,0.9858031123253609,0.71,3.794083607223553,200
+Linear,Linear,observational,True,4,0.95,0.8341666666666667,3.581621019228321,0.9858031123253609,0.78,4.334336049644241,200
 Linear,Linear,observational,True,6,0.9,0.8933333333333333,2.6626861416696204,0.7105550768719945,0.895,3.3880858319786715,200
+Linear,Linear,observational,True,6,0.95,0.9433333333333332,3.172786231124224,0.7105550768719945,0.935,3.871664400590925,200
diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
index a4dbdc4..db6a426 100644
--- a/results/did/did_multi_group.csv
+++ b/results/did/did_multi_group.csv
@@ -1,13 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
 Linear,Linear,experimental,False,1,0.9,0.8666666666666667,0.533698038133595,0.14197945807093054,0.85,0.6861047473750901,200
+Linear,Linear,experimental,False,1,0.95,0.935,0.6359404364145231,0.14197945807093054,0.9,0.7748893935196418,200
 Linear,Linear,experimental,False,4,0.9,0.66,2.1748653096663464,0.9342553462922274,0.525,2.745378335374161,200
+Linear,Linear,experimental,False,4,0.95,0.7633333333333333,2.5915118575455782,0.9342553462922274,0.655,3.1210909517446286,200
 Linear,Linear,experimental,False,6,0.9,0.8883333333333333,2.217594064950379,0.5525144231877022,0.875,2.791344195057897,200
+Linear,Linear,experimental,False,6,0.95,0.9366666666666668,2.642426309803645,0.5525144231877022,0.92,3.170576857891774,200
 Linear,Linear,experimental,True,1,0.9,0.87,0.5335807495502426,0.14168060865880763,0.865,0.6849043629713572,200
+Linear,Linear,experimental,True,1,0.95,0.9366666666666668,0.6358006784473694,0.14168060865880763,0.89,0.7736693416127448,200
 Linear,Linear,experimental,True,4,0.9,0.6633333333333333,2.173005344301668,0.9333928409584715,0.51,2.7419250872101033,200
+Linear,Linear,experimental,True,4,0.95,0.76,2.5892955721159634,0.9333928409584715,0.65,3.1048660668399184,200
 Linear,Linear,experimental,True,6,0.9,0.8933333333333333,2.2188654092146014,0.5513512630008591,0.85,2.790603998972855,200
+Linear,Linear,experimental,True,6,0.95,0.9366666666666668,2.6439412099315343,0.5513512630008591,0.93,3.172214838426176,200
 Linear,Linear,observational,False,1,0.9,0.8966666666666667,0.6108816472728611,0.145895094484495,0.87,0.783552984172717,200
+Linear,Linear,observational,False,1,0.95,0.9466666666666668,0.7279103792903224,0.145895094484495,0.92,0.8844690424185709,200
 Linear,Linear,observational,False,4,0.9,0.78,3.5092375556264455,1.0649254128150685,0.73,4.316704955507763,200
+Linear,Linear,observational,False,4,0.95,0.8466666666666667,4.181514457897795,1.0649254128150685,0.82,4.930150821324148,200
 Linear,Linear,observational,False,6,0.9,0.895,2.9637734181525097,0.7818480925391569,0.9,3.6693459962202515,200
+Linear,Linear,observational,False,6,0.95,0.9483333333333333,3.53155384937329,0.7818480925391569,0.96,4.202951707840176,200
 Linear,Linear,observational,True,1,0.9,0.895,0.5920636535297172,0.1408811699533362,0.885,0.7597493231438954,200
+Linear,Linear,observational,True,1,0.95,0.945,0.7054873567225215,0.1408811699533362,0.93,0.8582671047974608,200
 Linear,Linear,observational,True,4,0.9,0.7633333333333333,3.124012529892269,0.9973634763359689,0.72,3.880323357977367,200
+Linear,Linear,observational,True,4,0.95,0.84,3.7224905277370017,0.9973634763359689,0.81,4.430132897406105,200
 Linear,Linear,observational,True,6,0.9,0.8966666666666667,2.823741863032981,0.7474950667730065,0.88,3.5108185024043683,200
+Linear,Linear,observational,True,6,0.95,0.9466666666666668,3.3646959598709376,0.7474950667730065,0.945,4.0073726417113,200
diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
index 8f0a64a..0e83932 100644
--- a/results/did/did_multi_metadata.csv
+++ b/results/did/did_multi_metadata.csv
@@ -1,2 +1,2 @@
-DoubleML Version,Script,Date,Total Runtime (seconds),Python Version,Config File
-0.10.dev0,DIDMultiCoverageSimulation,2025-03-19 16:37:01,1605.1626930236816,3.12.9,scripts/did/multi_config.yml
+DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
+0.10.dev0,DIDMultiCoverageSimulation,2025-03-19 17:35,27.171297589937847,3.12.9,scripts/did/multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
index dd6f277..0e8a662 100644
--- a/results/did/did_multi_time.csv
+++ b/results/did/did_multi_time.csv
@@ -1,13 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
 Linear,Linear,experimental,False,1,0.9,0.85,0.49360461245245374,0.13317137989658068,0.805,0.6319610870324371,200
+Linear,Linear,experimental,False,1,0.95,0.9083333333333333,0.5881661730610652,0.13317137989658068,0.89,0.7144940237492544,200
 Linear,Linear,experimental,False,4,0.9,0.495,1.943482564791512,1.0099967068690494,0.475,2.237010089005512,200
+Linear,Linear,experimental,False,4,0.95,0.6183333333333334,2.315802311621298,1.0099967068690494,0.58,2.5967352695471275,200
 Linear,Linear,experimental,False,6,0.9,0.885,1.973586708219856,0.5137628285676683,0.875,2.269948492397086,200
+Linear,Linear,experimental,False,6,0.95,0.935,2.351673611011224,0.5137628285676683,0.93,2.6378921840020486,200
 Linear,Linear,experimental,True,1,0.9,0.8533333333333333,0.4936441210979332,0.13386449414919313,0.815,0.6316313581946184,200
+Linear,Linear,experimental,True,1,0.95,0.9083333333333333,0.5882132505158301,0.13386449414919313,0.89,0.7144700680586031,200
 Linear,Linear,experimental,True,4,0.9,0.495,1.9419691602716298,1.0073536527153972,0.485,2.233138294309388,200
+Linear,Linear,experimental,True,4,0.95,0.6033333333333334,2.313998978908644,1.0073536527153972,0.6,2.590827144962406,200
 Linear,Linear,experimental,True,6,0.9,0.8916666666666667,1.9743669935047528,0.5111362751423758,0.895,2.2727934396360774,200
+Linear,Linear,experimental,True,6,0.95,0.935,2.3526033782750138,0.5111362751423758,0.93,2.64045763785583,200
 Linear,Linear,observational,False,1,0.9,0.905,0.5957890718380163,0.14322442977409994,0.905,0.7621516555071253,200
+Linear,Linear,observational,False,1,0.95,0.9483333333333333,0.7099264664353688,0.14322442977409994,0.94,0.8622889926115689,200
 Linear,Linear,observational,False,4,0.9,0.7166666666666667,3.587198674078982,1.1518798523193952,0.695,4.005822809393348,200
+Linear,Linear,observational,False,4,0.95,0.7983333333333333,4.274410860263171,1.1518798523193952,0.785,4.68127664058613,200
 Linear,Linear,observational,False,6,0.9,0.8966666666666667,2.8422258436545302,0.7656607981855843,0.9,3.235109337869692,200
+Linear,Linear,observational,False,6,0.95,0.9466666666666668,3.386720981256164,0.7656607981855843,0.935,3.77638226426171,200
 Linear,Linear,observational,True,1,0.9,0.8966666666666667,0.5736643332685405,0.13819068965761655,0.89,0.7334447199193829,200
+Linear,Linear,observational,True,1,0.95,0.945,0.6835632143787673,0.13819068965761655,0.93,0.8284727058268883,200
 Linear,Linear,observational,True,4,0.9,0.7016666666666667,3.135225381339414,1.0623341589035538,0.66,3.525109938704791,200
+Linear,Linear,observational,True,4,0.95,0.7783333333333333,3.7358514643214518,1.0623341589035538,0.77,4.10980058127257,200
 Linear,Linear,observational,True,6,0.9,0.8816666666666667,2.6295968848181595,0.7121484510464997,0.87,3.011021831651718,200
+Linear,Linear,observational,True,6,0.95,0.935,3.1333579497006316,0.7121484510464997,0.95,3.4966663250213488,200

From 07968eea9369896640242ceb250eff03e953e9b7 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 19 Mar 2025 18:07:00 +0100
Subject: [PATCH 16/60] add aggregation sim to website

---
 doc/did/did_multi.qmd | 189 +++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 187 insertions(+), 2 deletions(-)

diff --git a/doc/did/did_multi.qmd b/doc/did/did_multi.qmd
index e2fc2ae..ef05bda 100644
--- a/doc/did/did_multi.qmd
+++ b/doc/did/did_multi.qmd
@@ -68,7 +68,7 @@ The simulations are based on the  the [make_did_SZ2020](https://docs.doubleml.or
 
 ```{python}
 #| echo: false
-metadata_file = '../../results/did/did_pa_multi_coverage_metadata.csv'
+metadata_file = '../../results/did/did_multi_metadata.csv'
 metadata_df = pd.read_csv(metadata_file)
 print(metadata_df.T.to_string(header=False))
 ```
@@ -79,7 +79,7 @@ print(metadata_df.T.to_string(header=False))
 #| echo: false
 
 # set up data
-df = pd.read_csv("../../results/did/did_pa_multi_coverage.csv", index_col=None)
+df = pd.read_csv("../../results/did/did_multi_detailed.csv", index_col=None)
 
 assert df["repetition"].nunique() == 1
 n_rep = df["repetition"].unique()[0]
@@ -106,3 +106,188 @@ level = 0.9
 df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
 make_pretty(df_ate_9, level, n_rep)
 ```
+
+
+### Experimental Score
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+## Aggregated Effects
+
+### Group Effects
+
+```{python}
+#| echo: false
+
+# set up data
+df = pd.read_csv("../../results/did/did_multi_group.csv", index_col=None)
+
+assert df["repetition"].nunique() == 1
+n_rep = df["repetition"].unique()[0]
+
+display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"]
+```
+
+#### Observational Score 
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+#### Experimental Score
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+### Time Effects
+
+```{python}
+#| echo: false
+
+# set up data
+df = pd.read_csv("../../results/did/did_multi_time.csv", index_col=None)
+
+assert df["repetition"].nunique() == 1
+n_rep = df["repetition"].unique()[0]
+
+display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"]
+```
+
+#### Observational Score 
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+#### Experimental Score
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+### Event Study Aggregation
+
+```{python}
+#| echo: false
+
+# set up data
+df = pd.read_csv("../../results/did/did_multi_eventstudy.csv", index_col=None)
+
+assert df["repetition"].nunique() == 1
+n_rep = df["repetition"].unique()[0]
+
+display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"]
+```
+
+#### Observational Score 
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+#### Experimental Score
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
\ No newline at end of file

From 7953be6fdbc562c9ffaadd11a1adae44dd3ab490 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Thu, 20 Mar 2025 17:57:31 +0100
Subject: [PATCH 17/60] update compute coverage

---
 monte-cover/src/montecover/base.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 6eccc73..9ab9571 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -206,22 +206,23 @@ def save_config(self, output_path: str):
     @staticmethod
     def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
         """Compute coverage, CI length, and bias."""
+        lower_bound = confint.iloc[:, 0]
+        upper_bound = confint.iloc[:, 1]
+        coverage_mask = (lower_bound < oracle_thetas) & (oracle_thetas < upper_bound)
 
-        coverage = np.mean((confint.iloc[:, 0] < oracle_thetas) & (oracle_thetas < confint.iloc[:, 1]))
-        ci_length = np.mean(confint.iloc[:, 1] - confint.iloc[:, 0])
-        bias = np.mean(abs(thetas - oracle_thetas))
         result_dict = {
-            "Coverage": coverage,
-            "CI Length": ci_length,
-            "Bias": bias,
+            "Coverage": np.mean(coverage_mask),
+            "CI Length": np.mean(upper_bound - lower_bound),
+            "Bias": np.mean(np.abs(thetas - oracle_thetas)),
         }
 
         if joint_confint is not None:
-            coverage_uniform = all((joint_confint.iloc[:, 0] < oracle_thetas) & (oracle_thetas < joint_confint.iloc[:, 1]))
-            ci_length_uniform = np.mean(joint_confint.iloc[:, 1] - joint_confint.iloc[:, 0])
+            joint_lower_bound = joint_confint.iloc[:, 0]
+            joint_upper_bound = joint_confint.iloc[:, 1]
+            joint_coverage_mark = (joint_lower_bound < oracle_thetas) & (oracle_thetas < joint_upper_bound)
 
-            result_dict["Uniform Coverage"] = coverage_uniform
-            result_dict["Uniform CI Length"] = ci_length_uniform
+            result_dict["Uniform Coverage"] = np.all(joint_coverage_mark)
+            result_dict["Uniform CI Length"] = np.mean(joint_upper_bound - joint_lower_bound)
 
         return result_dict
 

From 900f396e4e349245b60dd0c532376181a735ee37 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Thu, 20 Mar 2025 18:13:30 +0100
Subject: [PATCH 18/60] add initialize_simulation to run

---
 monte-cover/src/montecover/base.py | 88 ++++++++++++++++--------------
 1 file changed, 47 insertions(+), 41 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 9ab9571..bb8e694 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -78,23 +78,8 @@ def summarize_results(self) -> Dict[str, Any]:
 
     def run_simulation(self):
         """Run the full simulation."""
-        self.start_time = time.time()
-        self.logger.info("Starting simulation")
-
-        self.logger.info(f"DGP Parameters: {self.dgp_parameters}")
-        self.logger.info(f"DML Parameters: {self.dml_parameters}")
-        self.logger.info(f"Confidence Parameters: {self.confidence_parameters}")
-
-        # Calculate total number of parameter combinations
-        dgp_combinations = [len(v) for v in self.dgp_parameters.values()]
-        dml_combinations = [len(v) for v in self.dml_parameters.values()]
-        total_combinations = np.prod(dgp_combinations + dml_combinations)
-        self.logger.info(f"Total parameter combinations: {total_combinations}")
-
-        # Calculate expected total iterations
-        total_iterations = total_combinations * self.repetitions
-        self.logger.info(f"Expected total iterations: {total_iterations}")
-
+        self._initialize_simulation()
+        
         # Loop through repetitions
         for i_rep in range(self.repetitions):
             rep_start_time = time.time()
@@ -118,7 +103,7 @@ def run_simulation(self):
                     param_combo += 1
                     # Log parameter combination
                     self.logger.debug(
-                        f"Rep {i_rep+1}, Combo {param_combo}/{total_combinations}: DGPs {dgp_params}, DML {dml_params}"
+                        f"Rep {i_rep+1}, Combo {param_combo}/{self.total_combinations}: DGPs {dgp_params}, DML {dml_params}"
                     )
                     param_start_time = time.time()
 
@@ -166,6 +151,27 @@ def run_simulation(self):
         self.logger.info("Summarizing results")
         self.result_summary = self.summarize_results()
 
+    def _initialize_simulation(self):
+        """Initialize the simulation run."""
+        self.start_time = time.time()
+        self.logger.info("Starting simulation")
+        self.logger.info(f"DGP Parameters: {self.dgp_parameters}")
+        self.logger.info(f"DML Parameters: {self.dml_parameters}")
+        self.logger.info(f"Confidence Parameters: {self.confidence_parameters}")
+        
+        # Calculate expected iterations
+        self._calculate_parameter_combinations()
+
+    def _calculate_parameter_combinations(self):
+        """Calculate parameter combinations and expected iterations."""
+        dgp_combinations = [len(v) for v in self.dgp_parameters.values()]
+        dml_combinations = [len(v) for v in self.dml_parameters.values()]
+        self.total_combinations = np.prod(dgp_combinations + dml_combinations)
+        self.total_iterations = self.total_combinations * self.repetitions
+        
+        self.logger.info(f"Total parameter combinations: {self.total_combinations}")
+        self.logger.info(f"Expected total iterations: {self.total_iterations}")
+        
     def save_results(self, output_path: str = "results", file_prefix: str = ""):
         """Save the simulation results."""
         os.makedirs(output_path, exist_ok=True)
@@ -203,29 +209,6 @@ def save_config(self, output_path: str):
 
         self.logger.info(f"Configuration saved to {output_path}")
 
-    @staticmethod
-    def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
-        """Compute coverage, CI length, and bias."""
-        lower_bound = confint.iloc[:, 0]
-        upper_bound = confint.iloc[:, 1]
-        coverage_mask = (lower_bound < oracle_thetas) & (oracle_thetas < upper_bound)
-
-        result_dict = {
-            "Coverage": np.mean(coverage_mask),
-            "CI Length": np.mean(upper_bound - lower_bound),
-            "Bias": np.mean(np.abs(thetas - oracle_thetas)),
-        }
-
-        if joint_confint is not None:
-            joint_lower_bound = joint_confint.iloc[:, 0]
-            joint_upper_bound = joint_confint.iloc[:, 1]
-            joint_coverage_mark = (joint_lower_bound < oracle_thetas) & (oracle_thetas < joint_upper_bound)
-
-            result_dict["Uniform Coverage"] = np.all(joint_coverage_mark)
-            result_dict["Uniform CI Length"] = np.mean(joint_upper_bound - joint_lower_bound)
-
-        return result_dict
-
     def _load_config(self, config_path: str) -> Dict[str, Any]:
         """Load configuration from a YAML file."""
         if not os.path.exists(config_path):
@@ -270,3 +253,26 @@ def _setup_logging(self, log_level: str, log_file: Optional[str]):
             fh.setLevel(level)
             fh.setFormatter(formatter)
             self.logger.addHandler(fh)
+
+    @staticmethod
+    def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
+        """Compute coverage, CI length, and bias."""
+        lower_bound = confint.iloc[:, 0]
+        upper_bound = confint.iloc[:, 1]
+        coverage_mask = (lower_bound < oracle_thetas) & (oracle_thetas < upper_bound)
+
+        result_dict = {
+            "Coverage": np.mean(coverage_mask),
+            "CI Length": np.mean(upper_bound - lower_bound),
+            "Bias": np.mean(np.abs(thetas - oracle_thetas)),
+        }
+
+        if joint_confint is not None:
+            joint_lower_bound = joint_confint.iloc[:, 0]
+            joint_upper_bound = joint_confint.iloc[:, 1]
+            joint_coverage_mark = (joint_lower_bound < oracle_thetas) & (oracle_thetas < joint_upper_bound)
+
+            result_dict["Uniform Coverage"] = np.all(joint_coverage_mark)
+            result_dict["Uniform CI Length"] = np.mean(joint_upper_bound - joint_lower_bound)
+
+        return result_dict
\ No newline at end of file

From b23be4cfaaf949f8013915bee5fbc65573197027 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Thu, 20 Mar 2025 18:13:43 +0100
Subject: [PATCH 19/60] update log dir

---
 scripts/did/did_pa_multi_coverage.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/did/did_pa_multi_coverage.py b/scripts/did/did_pa_multi_coverage.py
index 22251f2..7d2a191 100644
--- a/scripts/did/did_pa_multi_coverage.py
+++ b/scripts/did/did_pa_multi_coverage.py
@@ -5,7 +5,7 @@
 sim = DIDMultiCoverageSimulation(
     config_file="scripts/did/multi_config.yml",
     log_level="INFO",
-    log_file="logs/current_sim.log"
+    log_file="logs/did/multi_sim.log"
 )
 sim.run_simulation()
 sim.save_results(output_path="results/did/", file_prefix="did_multi")

From 3f60e257ced64d95b2213e01aac3175ed3aeb014 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Thu, 20 Mar 2025 18:46:17 +0100
Subject: [PATCH 20/60] simplify base simulation logic

---
 monte-cover/src/montecover/base.py | 36 +++++++++++++++---------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index bb8e694..1a4d5b9 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -78,7 +78,7 @@ def summarize_results(self) -> Dict[str, Any]:
 
     def run_simulation(self):
         """Run the full simulation."""
-        self._initialize_simulation()
+        self._setup_simulation_metrics()
         
         # Loop through repetitions
         for i_rep in range(self.repetitions):
@@ -139,20 +139,10 @@ def run_simulation(self):
             rep_duration = rep_end_time - rep_start_time
             self.logger.info(f"Repetition {i_rep+1} completed in {rep_duration:.2f}s")
 
-        # convert results to dataframes
-        for key, value in self.results.items():
-            self.results[key] = pd.DataFrame(value)
-
-        self.end_time = time.time()
-        self.total_runtime = self.end_time - self.start_time
-        self.logger.info(f"Simulation completed in {self.total_runtime:.2f}s")
+        self._process_results()
 
-        # Summarize & save results
-        self.logger.info("Summarizing results")
-        self.result_summary = self.summarize_results()
-
-    def _initialize_simulation(self):
-        """Initialize the simulation run."""
+    def _setup_simulation_metrics(self):
+        """Initialize timing and calculate parameter combination metrics."""
         self.start_time = time.time()
         self.logger.info("Starting simulation")
         self.logger.info(f"DGP Parameters: {self.dgp_parameters}")
@@ -160,10 +150,6 @@ def _initialize_simulation(self):
         self.logger.info(f"Confidence Parameters: {self.confidence_parameters}")
         
         # Calculate expected iterations
-        self._calculate_parameter_combinations()
-
-    def _calculate_parameter_combinations(self):
-        """Calculate parameter combinations and expected iterations."""
         dgp_combinations = [len(v) for v in self.dgp_parameters.values()]
         dml_combinations = [len(v) for v in self.dml_parameters.values()]
         self.total_combinations = np.prod(dgp_combinations + dml_combinations)
@@ -171,7 +157,21 @@ def _calculate_parameter_combinations(self):
         
         self.logger.info(f"Total parameter combinations: {self.total_combinations}")
         self.logger.info(f"Expected total iterations: {self.total_iterations}")
+
+    def _process_results(self):
+        """Process collected results and log completion metrics."""
+        # Convert results to dataframes incrementally
+        for key, value in self.results.items():
+            self.results[key] = pd.DataFrame(value)
+        
+        self.end_time = time.time()
+        self.total_runtime = self.end_time - self.start_time
+        self.logger.info(f"Simulation completed in {self.total_runtime:.2f}s")
         
+        # Summarize results
+        self.logger.info("Summarizing results")
+        self.result_summary = self.summarize_results()
+
     def save_results(self, output_path: str = "results", file_prefix: str = ""):
         """Save the simulation results."""
         os.makedirs(output_path, exist_ok=True)

From fcd262f4bff6dc74810b8b21e26995723aebe37d Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 07:32:16 +0100
Subject: [PATCH 21/60] formatting

---
 monte-cover/src/montecover/base.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 1a4d5b9..6492486 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -79,7 +79,7 @@ def summarize_results(self) -> Dict[str, Any]:
     def run_simulation(self):
         """Run the full simulation."""
         self._setup_simulation_metrics()
-        
+
         # Loop through repetitions
         for i_rep in range(self.repetitions):
             rep_start_time = time.time()
@@ -148,13 +148,13 @@ def _setup_simulation_metrics(self):
         self.logger.info(f"DGP Parameters: {self.dgp_parameters}")
         self.logger.info(f"DML Parameters: {self.dml_parameters}")
         self.logger.info(f"Confidence Parameters: {self.confidence_parameters}")
-        
+
         # Calculate expected iterations
         dgp_combinations = [len(v) for v in self.dgp_parameters.values()]
         dml_combinations = [len(v) for v in self.dml_parameters.values()]
         self.total_combinations = np.prod(dgp_combinations + dml_combinations)
         self.total_iterations = self.total_combinations * self.repetitions
-        
+
         self.logger.info(f"Total parameter combinations: {self.total_combinations}")
         self.logger.info(f"Expected total iterations: {self.total_iterations}")
 
@@ -163,11 +163,11 @@ def _process_results(self):
         # Convert results to dataframes incrementally
         for key, value in self.results.items():
             self.results[key] = pd.DataFrame(value)
-        
+
         self.end_time = time.time()
         self.total_runtime = self.end_time - self.start_time
         self.logger.info(f"Simulation completed in {self.total_runtime:.2f}s")
-        
+
         # Summarize results
         self.logger.info("Summarizing results")
         self.result_summary = self.summarize_results()
@@ -275,4 +275,4 @@ def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
             result_dict["Uniform Coverage"] = np.all(joint_coverage_mark)
             result_dict["Uniform CI Length"] = np.mean(joint_upper_bound - joint_lower_bound)
 
-        return result_dict
\ No newline at end of file
+        return result_dict

From 988548091189a6aee3cc5b22b170eda6ac2ae9a2 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 07:45:49 +0100
Subject: [PATCH 22/60] rename parameter logging

---
 monte-cover/src/montecover/base.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 6492486..00a934d 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -78,7 +78,7 @@ def summarize_results(self) -> Dict[str, Any]:
 
     def run_simulation(self):
         """Run the full simulation."""
-        self._setup_simulation_metrics()
+        self._log_parameters()
 
         # Loop through repetitions
         for i_rep in range(self.repetitions):
@@ -141,7 +141,7 @@ def run_simulation(self):
 
         self._process_results()
 
-    def _setup_simulation_metrics(self):
+    def _log_parameters(self):
         """Initialize timing and calculate parameter combination metrics."""
         self.start_time = time.time()
         self.logger.info("Starting simulation")

From 7b6402986ea35f8289c4df0fc50c2d95f395679a Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 07:57:29 +0100
Subject: [PATCH 23/60] add stop simulation method

---
 monte-cover/src/montecover/base.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 00a934d..e221d55 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -85,10 +85,7 @@ def run_simulation(self):
             rep_start_time = time.time()
             self.logger.info(f"Starting repetition {i_rep + 1}/{self.repetitions}")
 
-            # Check elapsed time
-            elapsed_time = time.time() - self.start_time
-            if elapsed_time > self.max_runtime:
-                self.logger.warning("Maximum runtime exceeded. Stopping the simulation.")
+            if self._stop_simulation():
                 break
 
             param_combo = 0
@@ -158,6 +155,14 @@ def _log_parameters(self):
         self.logger.info(f"Total parameter combinations: {self.total_combinations}")
         self.logger.info(f"Expected total iterations: {self.total_iterations}")
 
+    def _stop_simulation(self) -> bool:
+        """Check if simulation should be stopped based on criteria like runtime."""
+        # Check if maximum runtime is exceeded
+        if self.max_runtime and time.time() - self.start_time > self.max_runtime:
+            self.logger.warning("Maximum runtime exceeded. Stopping the simulation.")
+            return True
+        return False
+
     def _process_results(self):
         """Process collected results and log completion metrics."""
         # Convert results to dataframes incrementally

From d538131b9eb3677abdc1d29e2ff6636c20e60215 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 08:07:12 +0100
Subject: [PATCH 24/60] simplify run_simulation

---
 monte-cover/src/montecover/base.py | 185 ++++++++++++++++-------------
 1 file changed, 103 insertions(+), 82 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index e221d55..d7c3d69 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -88,49 +88,8 @@ def run_simulation(self):
             if self._stop_simulation():
                 break
 
-            param_combo = 0
-            # loop through all
-            for dgp_param_values in product(*self.dgp_parameters.values()):
-                dgp_params = dict(zip(self.dgp_parameters.keys(), dgp_param_values))
-                dml_data = self._generate_dml_data(dgp_params)
-
-                for dml_param_values in product(*self.dml_parameters.values()):
-                    dml_params = dict(zip(self.dml_parameters.keys(), dml_param_values))
-
-                    param_combo += 1
-                    # Log parameter combination
-                    self.logger.debug(
-                        f"Rep {i_rep+1}, Combo {param_combo}/{self.total_combinations}: DGPs {dgp_params}, DML {dml_params}"
-                    )
-                    param_start_time = time.time()
-
-                    try:
-                        repetition_results = self.run_single_rep(dml_data, dml_params)
-                        param_end_time = time.time()
-                        param_duration = param_end_time - param_start_time
-
-                        if repetition_results is not None:
-                            assert isinstance(repetition_results, dict), "The result must be a dictionary."
-                            # Process each dataframe in the result dictionary
-                            for result_name, repetition_result in repetition_results.items():
-                                assert isinstance(repetition_result, list), "Each repetition_result must be a list."
-                                for res in repetition_result:
-                                    assert isinstance(res, dict), "Each res must be a dictionary."
-                                    res["repetition"] = i_rep
-                                    # add dgp parameters to the result
-                                    res.update(dgp_params)
-
-                                # Initialize key in results dict if not exists
-                                if result_name not in self.results:
-                                    self.results[result_name] = []
-                                self.results[result_name].extend(repetition_result)
-
-                            self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s")
-                    except Exception as e:
-                        self.logger.error(
-                            f"Error: repetition {i_rep+1}, DGP parameters {dgp_params}, DML parameters {dml_params}: {str(e)}"
-                        )
-                        self.logger.exception("Exception details:")
+            # Running actual simulation
+            self._process_repetition(i_rep)
 
             rep_end_time = time.time()
             rep_duration = rep_end_time - rep_start_time
@@ -138,45 +97,6 @@ def run_simulation(self):
 
         self._process_results()
 
-    def _log_parameters(self):
-        """Initialize timing and calculate parameter combination metrics."""
-        self.start_time = time.time()
-        self.logger.info("Starting simulation")
-        self.logger.info(f"DGP Parameters: {self.dgp_parameters}")
-        self.logger.info(f"DML Parameters: {self.dml_parameters}")
-        self.logger.info(f"Confidence Parameters: {self.confidence_parameters}")
-
-        # Calculate expected iterations
-        dgp_combinations = [len(v) for v in self.dgp_parameters.values()]
-        dml_combinations = [len(v) for v in self.dml_parameters.values()]
-        self.total_combinations = np.prod(dgp_combinations + dml_combinations)
-        self.total_iterations = self.total_combinations * self.repetitions
-
-        self.logger.info(f"Total parameter combinations: {self.total_combinations}")
-        self.logger.info(f"Expected total iterations: {self.total_iterations}")
-
-    def _stop_simulation(self) -> bool:
-        """Check if simulation should be stopped based on criteria like runtime."""
-        # Check if maximum runtime is exceeded
-        if self.max_runtime and time.time() - self.start_time > self.max_runtime:
-            self.logger.warning("Maximum runtime exceeded. Stopping the simulation.")
-            return True
-        return False
-
-    def _process_results(self):
-        """Process collected results and log completion metrics."""
-        # Convert results to dataframes incrementally
-        for key, value in self.results.items():
-            self.results[key] = pd.DataFrame(value)
-
-        self.end_time = time.time()
-        self.total_runtime = self.end_time - self.start_time
-        self.logger.info(f"Simulation completed in {self.total_runtime:.2f}s")
-
-        # Summarize results
-        self.logger.info("Summarizing results")
-        self.result_summary = self.summarize_results()
-
     def save_results(self, output_path: str = "results", file_prefix: str = ""):
         """Save the simulation results."""
         os.makedirs(output_path, exist_ok=True)
@@ -259,6 +179,107 @@ def _setup_logging(self, log_level: str, log_file: Optional[str]):
             fh.setFormatter(formatter)
             self.logger.addHandler(fh)
 
+    def _log_parameters(self):
+        """Initialize timing and calculate parameter combination metrics."""
+        self.start_time = time.time()
+        self.logger.info("Starting simulation")
+        self.logger.info(f"DGP Parameters: {self.dgp_parameters}")
+        self.logger.info(f"DML Parameters: {self.dml_parameters}")
+        self.logger.info(f"Confidence Parameters: {self.confidence_parameters}")
+
+        # Calculate expected iterations
+        dgp_combinations = [len(v) for v in self.dgp_parameters.values()]
+        dml_combinations = [len(v) for v in self.dml_parameters.values()]
+        self.total_combinations = np.prod(dgp_combinations + dml_combinations)
+        self.total_iterations = self.total_combinations * self.repetitions
+
+        self.logger.info(f"Total parameter combinations: {self.total_combinations}")
+        self.logger.info(f"Expected total iterations: {self.total_iterations}")
+
+    def _stop_simulation(self) -> bool:
+        """Check if simulation should be stopped based on criteria like runtime."""
+        # Check if maximum runtime is exceeded
+        if self.max_runtime and time.time() - self.start_time > self.max_runtime:
+            self.logger.warning("Maximum runtime exceeded. Stopping the simulation.")
+            return True
+        return False
+
+    def _process_repetition(self, i_rep):
+        """Process a single repetition with all parameter combinations."""
+        i_param_combo = 0
+
+        # loop through all parameter combinations
+        for dgp_param_values in product(*self.dgp_parameters.values()):
+            dgp_params = dict(zip(self.dgp_parameters.keys(), dgp_param_values))
+            dml_data = self._generate_dml_data(dgp_params)
+
+            for dml_param_values in product(*self.dml_parameters.values()):
+                dml_params = dict(zip(self.dml_parameters.keys(), dml_param_values))
+                i_param_combo += 1
+
+                self._process_parameter_combination(i_rep, i_param_combo, dgp_params, dml_params, dml_data)
+
+    def _process_parameter_combination(self, i_rep, param_combo, dgp_params, dml_params, dml_data):
+        """Process a single parameter combination."""
+        # Log parameter combination
+        self.logger.debug(
+            f"Rep {i_rep+1}, Combo {param_combo}/{self.total_combinations}: " f"DGPs {dgp_params}, DML {dml_params}"
+        )
+        param_start_time = time.time()
+
+        try:
+            repetition_results = self.run_single_rep(dml_data, dml_params)
+
+            # Log timing
+            param_duration = time.time() - param_start_time
+            self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s")
+
+            # Store results
+            self._store_results(repetition_results, i_rep, dgp_params)
+
+        except Exception as e:
+            self.logger.error(
+                f"Error: repetition {i_rep+1}, DGP parameters {dgp_params}, " f"DML parameters {dml_params}: {str(e)}"
+            )
+            self.logger.exception("Exception details:")
+
+    def _store_results(self, repetition_results, i_rep, dgp_params):
+        """Store the results of a repetition."""
+        if repetition_results is None:
+            return
+
+        assert isinstance(repetition_results, dict), "The result must be a dictionary."
+
+        # Process each dataframe in the result dictionary
+        for result_name, repetition_result in repetition_results.items():
+            assert isinstance(repetition_result, list), "Each repetition_result must be a list."
+
+            for res in repetition_result:
+                assert isinstance(res, dict), "Each res must be a dictionary."
+                res["repetition"] = i_rep
+                # add dgp parameters to the result
+                res.update(dgp_params)
+
+            # Initialize key in results dict if not exists
+            if result_name not in self.results:
+                self.results[result_name] = []
+
+            self.results[result_name].extend(repetition_result)
+
+    def _process_results(self):
+        """Process collected results and log completion metrics."""
+        # Convert results to dataframes incrementally
+        for key, value in self.results.items():
+            self.results[key] = pd.DataFrame(value)
+
+        self.end_time = time.time()
+        self.total_runtime = self.end_time - self.start_time
+        self.logger.info(f"Simulation completed in {self.total_runtime:.2f}s")
+
+        # Summarize results
+        self.logger.info("Summarizing results")
+        self.result_summary = self.summarize_results()
+
     @staticmethod
     def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
         """Compute coverage, CI length, and bias."""

From a01ee8c2321ee2d96d83065c43fdc81af4ad70d8 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 08:49:41 +0100
Subject: [PATCH 25/60] allow parallel simulations

---
 monte-cover/src/montecover/base.py | 130 ++++++++++++++++++-----------
 1 file changed, 83 insertions(+), 47 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index d7c3d69..19ac623 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -12,6 +12,7 @@
 import numpy as np
 import pandas as pd
 import yaml
+from joblib import Parallel, delayed
 
 
 class BaseSimulation(ABC):
@@ -33,6 +34,7 @@ def __init__(
         self.repetitions = self.simulation_parameters.get("repetitions", 20)
         self.max_runtime = self.simulation_parameters.get("max_runtime", 5.5 * 3600)
         self.random_seed = self.simulation_parameters.get("random_seed", 42)
+        self.default_n_jobs = self.simulation_parameters.get("n_jobs", 1)
         self.suppress_warnings = suppress_warnings
 
         # Set up logging
@@ -76,24 +78,55 @@ def summarize_results(self) -> Dict[str, Any]:
         """Summarize the simulation results."""
         pass
 
-    def run_simulation(self):
-        """Run the full simulation."""
-        self._log_parameters()
-
-        # Loop through repetitions
-        for i_rep in range(self.repetitions):
-            rep_start_time = time.time()
-            self.logger.info(f"Starting repetition {i_rep + 1}/{self.repetitions}")
-
-            if self._stop_simulation():
-                break
+    def run_simulation(self, n_jobs=None):
+        """
+        Run the full simulation.
+
+        Parameters:
+        -----------
+        n_jobs : int, optional (default=None)
+            Number of jobs to run in parallel. If None, uses the value from the configuration file.
+            If 1, runs sequentially. If > 1, runs in parallel with the specified number of workers.
+            If -1, uses all available CPU cores (except one).
+        """
+        # Use n_jobs from parameter, or fall back to config value
+        n_jobs = n_jobs if n_jobs is not None else self.default_n_jobs
+        if n_jobs == -1:
+            n_jobs = os.cpu_count() - 1
+        self._log_parameters(n_jobs=n_jobs)
+
+        if n_jobs <= 1:
+            for i_rep in range(self.repetitions):
+                rep_start_time = time.time()
+                self.logger.info(f"Starting repetition {i_rep + 1}/{self.repetitions}")
+
+                if self._stop_simulation():
+                    break
+
+                # Running actual simulation
+                self._process_repetition(i_rep)
+
+                rep_end_time = time.time()
+                rep_duration = rep_end_time - rep_start_time
+                self.logger.info(f"Repetition {i_rep+1} completed in {rep_duration:.2f}s")
+        else:
 
-            # Running actual simulation
-            self._process_repetition(i_rep)
+            repetitions_to_run = list(range(self.repetitions))
+            
+            self.logger.info(f"Starting parallel execution with {n_jobs} workers")
+            results = Parallel(n_jobs=n_jobs, verbose=10)(
+                delayed(self._process_repetition)(i_rep)
+                for i_rep in repetitions_to_run
+                if not self._stop_simulation()
+            )
 
-            rep_end_time = time.time()
-            rep_duration = rep_end_time - rep_start_time
-            self.logger.info(f"Repetition {i_rep+1} completed in {rep_duration:.2f}s")
+            # Process results from parallel execution
+            for worker_results in results:
+                if worker_results:  # Check if we have results
+                    for result_name, result_list in worker_results.items():
+                        if result_name not in self.results:
+                            self.results[result_name] = []
+                        self.results[result_name].extend(result_list)
 
         self._process_results()
 
@@ -179,7 +212,7 @@ def _setup_logging(self, log_level: str, log_file: Optional[str]):
             fh.setFormatter(formatter)
             self.logger.addHandler(fh)
 
-    def _log_parameters(self):
+    def _log_parameters(self, n_jobs):
         """Initialize timing and calculate parameter combination metrics."""
         self.start_time = time.time()
         self.logger.info("Starting simulation")
@@ -195,6 +228,10 @@ def _log_parameters(self):
 
         self.logger.info(f"Total parameter combinations: {self.total_combinations}")
         self.logger.info(f"Expected total iterations: {self.total_iterations}")
+        if n_jobs <= 1:
+            self.logger.info("Running simulation sequentially")
+        else:
+            self.logger.info(f"Running simulation in parallel with {n_jobs} workers")
 
     def _stop_simulation(self) -> bool:
         """Check if simulation should be stopped based on criteria like runtime."""
@@ -206,7 +243,8 @@ def _stop_simulation(self) -> bool:
 
     def _process_repetition(self, i_rep):
         """Process a single repetition with all parameter combinations."""
-        i_param_combo = 0
+        i_param_comb = 0
+        rep_results = {}
 
         # loop through all parameter combinations
         for dgp_param_values in product(*self.dgp_parameters.values()):
@@ -215,15 +253,23 @@ def _process_repetition(self, i_rep):
 
             for dml_param_values in product(*self.dml_parameters.values()):
                 dml_params = dict(zip(self.dml_parameters.keys(), dml_param_values))
-                i_param_combo += 1
+                i_param_comb += 1
 
-                self._process_parameter_combination(i_rep, i_param_combo, dgp_params, dml_params, dml_data)
+                comb_results = self._process_parameter_combination(i_rep, i_param_comb, dgp_params, dml_params, dml_data)
 
-    def _process_parameter_combination(self, i_rep, param_combo, dgp_params, dml_params, dml_data):
+                # Merge results
+                for result_name, result_list in comb_results.items():
+                    if result_name not in rep_results:
+                        rep_results[result_name] = []
+                    rep_results[result_name].extend(result_list)
+    
+        return rep_results
+
+    def _process_parameter_combination(self, i_rep, i_param_comb, dgp_params, dml_params, dml_data):
         """Process a single parameter combination."""
         # Log parameter combination
         self.logger.debug(
-            f"Rep {i_rep+1}, Combo {param_combo}/{self.total_combinations}: " f"DGPs {dgp_params}, DML {dml_params}"
+            f"Rep {i_rep+1}, Combo {i_param_comb}/{self.total_combinations}: " f"DGPs {dgp_params}, DML {dml_params}"
         )
         param_start_time = time.time()
 
@@ -234,37 +280,27 @@ def _process_parameter_combination(self, i_rep, param_combo, dgp_params, dml_par
             param_duration = time.time() - param_start_time
             self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s")
 
-            # Store results
-            self._store_results(repetition_results, i_rep, dgp_params)
+            # Process results
+            if repetition_results is None:
+                return {}
+                
+            # Add metadata to results
+            processed_results = {}
+            for result_name, repetition_result in repetition_results.items():
+                processed_results[result_name] = []
+                for res in repetition_result:
+                    res["repetition"] = i_rep
+                    res.update(dgp_params)
+                    processed_results[result_name].append(res)
+                    
+            return processed_results
 
         except Exception as e:
             self.logger.error(
                 f"Error: repetition {i_rep+1}, DGP parameters {dgp_params}, " f"DML parameters {dml_params}: {str(e)}"
             )
             self.logger.exception("Exception details:")
-
-    def _store_results(self, repetition_results, i_rep, dgp_params):
-        """Store the results of a repetition."""
-        if repetition_results is None:
-            return
-
-        assert isinstance(repetition_results, dict), "The result must be a dictionary."
-
-        # Process each dataframe in the result dictionary
-        for result_name, repetition_result in repetition_results.items():
-            assert isinstance(repetition_result, list), "Each repetition_result must be a list."
-
-            for res in repetition_result:
-                assert isinstance(res, dict), "Each res must be a dictionary."
-                res["repetition"] = i_rep
-                # add dgp parameters to the result
-                res.update(dgp_params)
-
-            # Initialize key in results dict if not exists
-            if result_name not in self.results:
-                self.results[result_name] = []
-
-            self.results[result_name].extend(repetition_result)
+            return {}
 
     def _process_results(self):
         """Process collected results and log completion metrics."""

From e6feb4824952665b42fe0927d3f7d9a1ce3ed361 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 08:51:00 +0100
Subject: [PATCH 26/60] remove parallel fold fit

---
 monte-cover/src/montecover/did/did_multi.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_multi.py
index 4eb6af0..b10f6ce 100644
--- a/monte-cover/src/montecover/did/did_multi.py
+++ b/monte-cover/src/montecover/did/did_multi.py
@@ -98,7 +98,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
             score=score,
             in_sample_normalization=in_sample_normalization,
         )
-        dml_model.fit(n_jobs_cv=5)
+        dml_model.fit()
         dml_model.bootstrap(n_rep_boot=2000)
 
         # Oracle values for this model

From 55494bfd145ae75172adbbdd06ed1d8c99c9c519 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 11:58:19 +0100
Subject: [PATCH 27/60] handle warnings and jobs

---
 monte-cover/src/montecover/base.py | 26 ++++++++++++--------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 19ac623..8cbadf4 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -87,15 +87,14 @@ def run_simulation(self, n_jobs=None):
         n_jobs : int, optional (default=None)
             Number of jobs to run in parallel. If None, uses the value from the configuration file.
             If 1, runs sequentially. If > 1, runs in parallel with the specified number of workers.
-            If -1, uses all available CPU cores (except one).
+            If -1, uses all available CPU cores .
+            If -2, uses all available CPU cores except one.
         """
         # Use n_jobs from parameter, or fall back to config value
         n_jobs = n_jobs if n_jobs is not None else self.default_n_jobs
-        if n_jobs == -1:
-            n_jobs = os.cpu_count() - 1
         self._log_parameters(n_jobs=n_jobs)
 
-        if n_jobs <= 1:
+        if n_jobs == 1:
             for i_rep in range(self.repetitions):
                 rep_start_time = time.time()
                 self.logger.info(f"Starting repetition {i_rep + 1}/{self.repetitions}")
@@ -109,15 +108,11 @@ def run_simulation(self, n_jobs=None):
                 rep_end_time = time.time()
                 rep_duration = rep_end_time - rep_start_time
                 self.logger.info(f"Repetition {i_rep+1} completed in {rep_duration:.2f}s")
-        else:
 
-            repetitions_to_run = list(range(self.repetitions))
-            
-            self.logger.info(f"Starting parallel execution with {n_jobs} workers")
+        else:
+            self.logger.info(f"Starting parallel execution with n_jobs={n_jobs}")
             results = Parallel(n_jobs=n_jobs, verbose=10)(
-                delayed(self._process_repetition)(i_rep)
-                for i_rep in repetitions_to_run
-                if not self._stop_simulation()
+                delayed(self._process_repetition)(i_rep) for i_rep in range(self.repetitions) if not self._stop_simulation()
             )
 
             # Process results from parallel execution
@@ -243,6 +238,9 @@ def _stop_simulation(self) -> bool:
 
     def _process_repetition(self, i_rep):
         """Process a single repetition with all parameter combinations."""
+        if self.suppress_warnings:
+            warnings.simplefilter(action="ignore", category=UserWarning)
+
         i_param_comb = 0
         rep_results = {}
 
@@ -262,7 +260,7 @@ def _process_repetition(self, i_rep):
                     if result_name not in rep_results:
                         rep_results[result_name] = []
                     rep_results[result_name].extend(result_list)
-    
+
         return rep_results
 
     def _process_parameter_combination(self, i_rep, i_param_comb, dgp_params, dml_params, dml_data):
@@ -283,7 +281,7 @@ def _process_parameter_combination(self, i_rep, i_param_comb, dgp_params, dml_pa
             # Process results
             if repetition_results is None:
                 return {}
-                
+
             # Add metadata to results
             processed_results = {}
             for result_name, repetition_result in repetition_results.items():
@@ -292,7 +290,7 @@ def _process_parameter_combination(self, i_rep, i_param_comb, dgp_params, dml_pa
                     res["repetition"] = i_rep
                     res.update(dgp_params)
                     processed_results[result_name].append(res)
-                    
+
             return processed_results
 
         except Exception as e:

From dbc092e73826bad596a1786466316cf99819642d Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 12:57:59 +0100
Subject: [PATCH 28/60] add inner_max_num_threads=1

---
 monte-cover/src/montecover/base.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index 8cbadf4..c312e0a 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -12,7 +12,7 @@
 import numpy as np
 import pandas as pd
 import yaml
-from joblib import Parallel, delayed
+from joblib import Parallel, delayed, parallel_backend
 
 
 class BaseSimulation(ABC):
@@ -111,9 +111,10 @@ def run_simulation(self, n_jobs=None):
 
         else:
             self.logger.info(f"Starting parallel execution with n_jobs={n_jobs}")
-            results = Parallel(n_jobs=n_jobs, verbose=10)(
-                delayed(self._process_repetition)(i_rep) for i_rep in range(self.repetitions) if not self._stop_simulation()
-            )
+            with parallel_backend("loky", inner_max_num_threads=1):
+                results = Parallel(n_jobs=n_jobs, verbose=10)(
+                    delayed(self._process_repetition)(i_rep) for i_rep in range(self.repetitions) if not self._stop_simulation()
+                )
 
             # Process results from parallel execution
             for worker_results in results:

From 79320ed96f746364c72e11fe44f5ff92b8dcd4c8 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 13:04:00 +0100
Subject: [PATCH 29/60] update did simulation

---
 results/did/did_multi_detailed.csv   | 48 ++++++++++++++--------------
 results/did/did_multi_eventstudy.csv | 48 ++++++++++++++--------------
 results/did/did_multi_group.csv      | 48 ++++++++++++++--------------
 results/did/did_multi_metadata.csv   |  2 +-
 results/did/did_multi_time.csv       | 48 ++++++++++++++--------------
 results/did/multi_config.yml         |  3 +-
 scripts/did/did_pa_multi_coverage.py |  2 +-
 scripts/did/multi_config.yml         |  3 +-
 8 files changed, 102 insertions(+), 100 deletions(-)

diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
index f37be82..3d0b81a 100644
--- a/results/did/did_multi_detailed.csv
+++ b/results/did/did_multi_detailed.csv
@@ -1,25 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.8770833333333333,0.5957148258014461,0.15382974604923927,0.82,0.9277609206218338,200
-Linear,Linear,experimental,False,1,0.95,0.9329166666666667,0.709837996826105,0.15382974604923927,0.9,1.0183441020334203,200
-Linear,Linear,experimental,False,4,0.9,0.6683333333333333,1.9698836561519315,0.8284907405553992,0.525,2.863183256412114,200
-Linear,Linear,experimental,False,4,0.95,0.7670833333333333,2.347261152317584,0.8284907405553992,0.64,3.1905243140809945,200
-Linear,Linear,experimental,False,6,0.9,0.89375,2.012015821768593,0.4972286199594851,0.9,2.910046927597859,200
-Linear,Linear,experimental,False,6,0.95,0.9441666666666667,2.3974647241407983,0.4972286199594851,0.935,3.246758702976773,200
-Linear,Linear,experimental,True,1,0.9,0.8745833333333333,0.5956334541132615,0.1540676164908103,0.82,0.929071986266196,200
-Linear,Linear,experimental,True,1,0.95,0.9333333333333332,0.7097410364792451,0.1540676164908103,0.895,1.0214045871405517,200
-Linear,Linear,experimental,True,4,0.9,0.6695833333333333,1.9690878328880732,0.8280711666335188,0.515,2.8592795655154974,200
-Linear,Linear,experimental,True,4,0.95,0.7670833333333333,2.346312870409903,0.8280711666335188,0.645,3.184749486936283,200
-Linear,Linear,experimental,True,6,0.9,0.8954166666666667,2.012277261422281,0.4972240370172027,0.905,2.910046165626694,200
-Linear,Linear,experimental,True,6,0.95,0.9433333333333332,2.3977762487025966,0.4972240370172027,0.93,3.2443247059312212,200
-Linear,Linear,observational,False,1,0.9,0.89625,0.686900650227242,0.16556887731250863,0.905,1.0684577495769967,200
-Linear,Linear,observational,False,1,0.95,0.9441666666666667,0.8184926083044464,0.16556887731250863,0.93,1.1743444720583638,200
-Linear,Linear,observational,False,4,0.9,0.7866666666666667,3.124729480506762,0.9502826142586591,0.735,4.394633218514321,200
-Linear,Linear,observational,False,4,0.95,0.8541666666666667,3.7233448270862413,0.9502826142586591,0.8,4.923945197030173,200
-Linear,Linear,observational,False,6,0.9,0.8970833333333332,2.773055872422081,0.7355132665655921,0.915,3.9680250024995116,200
-Linear,Linear,observational,False,6,0.95,0.9541666666666667,3.304299876906268,0.7355132665655921,0.95,4.437925510709768,200
-Linear,Linear,observational,True,1,0.9,0.88875,0.6660387052074566,0.16288234355298709,0.875,1.03599720010251,200
-Linear,Linear,observational,True,1,0.95,0.9408333333333333,0.7936340675709367,0.16288234355298709,0.925,1.1385204090656658,200
-Linear,Linear,observational,True,4,0.9,0.7770833333333332,2.832512839406202,0.8935845118739927,0.72,4.034877974695546,200
-Linear,Linear,observational,True,4,0.95,0.8545833333333333,3.375147222839926,0.8935845118739927,0.78,4.5253801160151035,200
-Linear,Linear,observational,True,6,0.9,0.8975,2.5644621837469375,0.675326608841152,0.9,3.688249907793509,200
-Linear,Linear,observational,True,6,0.95,0.9470833333333333,3.055745166318817,0.675326608841152,0.94,4.115336505261702,200
+Linear,Linear,experimental,False,1,0.9,0.8813333333333334,0.5961686505266492,0.15102133299318632,0.834,0.9296789699097547,1000
+Linear,Linear,experimental,False,1,0.95,0.9350833333333334,0.7103787623398975,0.15102133299318632,0.908,1.0206042134000024,1000
+Linear,Linear,experimental,False,4,0.9,0.6619166666666666,1.986651429023609,0.8637379841284726,0.52,2.882026229739649,1000
+Linear,Linear,experimental,False,4,0.95,0.7520833333333333,2.3672411860366602,0.8637379841284726,0.617,3.2128015018838694,1000
+Linear,Linear,experimental,False,6,0.9,0.902,2.00497022725443,0.4810970023674208,0.911,2.9031760219044904,1000
+Linear,Linear,experimental,False,6,0.95,0.9528333333333334,2.3890693804633027,0.4810970023674208,0.951,3.238600319213653,1000
+Linear,Linear,experimental,True,1,0.9,0.8800833333333333,0.5960959285920073,0.15123045002955704,0.833,0.928366902428871,1000
+Linear,Linear,experimental,True,1,0.95,0.9353333333333333,0.7102921088100949,0.15123045002955704,0.913,1.0197692316323934,1000
+Linear,Linear,experimental,True,4,0.9,0.6590833333333334,1.9857772419115973,0.8626296646616975,0.52,2.8815675975045956,1000
+Linear,Linear,experimental,True,4,0.95,0.7531666666666667,2.3661995278445764,0.8626296646616975,0.619,3.2112254886896605,1000
+Linear,Linear,experimental,True,6,0.9,0.9019166666666666,2.004527154284187,0.4808213402748324,0.905,2.9011439332421705,1000
+Linear,Linear,experimental,True,6,0.95,0.952,2.3885414264557423,0.4808213402748324,0.949,3.237049999080413,1000
+Linear,Linear,observational,False,1,0.9,0.90575,0.6906210074073058,0.16339132921843413,0.897,1.0727694533982786,1000
+Linear,Linear,observational,False,1,0.95,0.9518333333333334,0.8229256873110352,0.16339132921843413,0.946,1.1792381527650921,1000
+Linear,Linear,observational,False,4,0.9,0.7795,2.9571361297998364,0.9287818108191471,0.691,4.19740184270945,1000
+Linear,Linear,observational,False,4,0.95,0.8571666666666666,3.5236450324955486,0.9287818108191471,0.78,4.697698545475343,1000
+Linear,Linear,observational,False,6,0.9,0.9076666666666666,2.5544833881976365,0.6416394592068319,0.907,3.6632944671996808,1000
+Linear,Linear,observational,False,6,0.95,0.9550833333333334,3.0438546980332184,0.6416394592068319,0.958,4.0959649390037125,1000
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+Linear,Linear,observational,True,4,0.95,0.8508333333333333,3.3639582144020532,0.9146880810616262,0.768,4.499697598146944,1000
+Linear,Linear,observational,True,6,0.9,0.89775,2.4371258648326704,0.6190705910995143,0.893,3.5036401350107074,1000
+Linear,Linear,observational,True,6,0.95,0.9521666666666666,2.9040145837876374,0.6190705910995143,0.955,3.9130910221754434,1000
diff --git a/results/did/did_multi_eventstudy.csv b/results/did/did_multi_eventstudy.csv
index 5d8b92f..78a9bb7 100644
--- a/results/did/did_multi_eventstudy.csv
+++ b/results/did/did_multi_eventstudy.csv
@@ -1,25 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.8833333333333333,0.42565718743306286,0.10936767904635211,0.835,0.6077012485542163,200
-Linear,Linear,experimental,False,1,0.95,0.9358333333333333,0.507201822374698,0.10936767904635211,0.905,0.6747323911476379,200
-Linear,Linear,experimental,False,4,0.9,0.6125,1.9569891887347262,0.9196943825351817,0.545,2.5358161709127596,200
-Linear,Linear,experimental,False,4,0.95,0.7141666666666667,2.3318964467149415,0.9196943825351817,0.635,2.8827181581319787,200
-Linear,Linear,experimental,False,6,0.9,0.8975,2.0153850665398654,0.5111684848078778,0.88,2.5922545970785786,200
-Linear,Linear,experimental,False,6,0.95,0.9441666666666667,2.40147942690741,0.5111684848078778,0.94,2.9541718057018187,200
-Linear,Linear,experimental,True,1,0.9,0.8791666666666668,0.4255897490005149,0.10926516710603056,0.845,0.6066297468359033,200
-Linear,Linear,experimental,True,1,0.95,0.9358333333333333,0.5071214645259496,0.10926516710603056,0.915,0.6750610192677344,200
-Linear,Linear,experimental,True,4,0.9,0.605,1.9564893215757844,0.9195779385934096,0.55,2.5307678617965537,200
-Linear,Linear,experimental,True,4,0.95,0.7158333333333333,2.3313008182574744,0.9195779385934096,0.65,2.872282262053714,200
-Linear,Linear,experimental,True,6,0.9,0.8941666666666667,2.015463986254987,0.5113312788664856,0.89,2.5944084708379807,200
-Linear,Linear,experimental,True,6,0.95,0.9466666666666668,2.401573465548158,0.5113312788664856,0.93,2.9505612073229464,200
-Linear,Linear,observational,False,1,0.9,0.9083333333333333,0.4882437838128583,0.11428563471509198,0.9,0.6956288268269425,200
-Linear,Linear,observational,False,1,0.95,0.9533333333333333,0.5817783517444832,0.11428563471509198,0.945,0.7729973931119418,200
-Linear,Linear,observational,False,4,0.9,0.7691666666666667,3.3932130254913604,1.0549489318868936,0.755,4.223665366280769,200
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-Linear,Linear,observational,False,6,0.9,0.8958333333333333,2.8512971267032157,0.7733310576205062,0.895,3.607707492218858,200
-Linear,Linear,observational,False,6,0.95,0.955,3.397530081699918,0.7733310576205062,0.955,4.122679418390554,200
-Linear,Linear,observational,True,1,0.9,0.8975,0.4745126476352591,0.11192315736615333,0.895,0.6757224816274575,200
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-Linear,Linear,observational,True,4,0.9,0.7566666666666667,3.005791111628938,0.9858031123253609,0.71,3.794083607223553,200
-Linear,Linear,observational,True,4,0.95,0.8341666666666667,3.581621019228321,0.9858031123253609,0.78,4.334336049644241,200
-Linear,Linear,observational,True,6,0.9,0.8933333333333333,2.6626861416696204,0.7105550768719945,0.895,3.3880858319786715,200
-Linear,Linear,observational,True,6,0.95,0.9433333333333332,3.172786231124224,0.7105550768719945,0.935,3.871664400590925,200
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+Linear,Linear,experimental,True,1,0.95,0.9338333333333334,0.50675000471846,0.11135661295209692,0.919,0.6737727543313963,1000
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+Linear,Linear,experimental,True,4,0.95,0.69,2.356735425573614,0.9610017220191109,0.614,2.906700104748762,1000
+Linear,Linear,experimental,True,6,0.9,0.9028333333333334,2.001368819203873,0.47488976645105485,0.91,2.5788107797213202,1000
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+Linear,Linear,observational,True,4,0.9,0.7318333333333333,2.961887231450272,1.0118391967276548,0.664,3.755668505865932,1000
+Linear,Linear,observational,True,4,0.95,0.8186666666666667,3.5293063192928438,1.0118391967276548,0.771,4.287491165909032,1000
+Linear,Linear,observational,True,6,0.9,0.8983333333333333,2.4694251965466845,0.6253200792538618,0.908,3.1600238226116533,1000
+Linear,Linear,observational,True,6,0.95,0.9541666666666666,2.9425016113546496,0.6253200792538618,0.955,3.596198019745218,1000
diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
index db6a426..d9fa94b 100644
--- a/results/did/did_multi_group.csv
+++ b/results/did/did_multi_group.csv
@@ -1,25 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.8666666666666667,0.533698038133595,0.14197945807093054,0.85,0.6861047473750901,200
-Linear,Linear,experimental,False,1,0.95,0.935,0.6359404364145231,0.14197945807093054,0.9,0.7748893935196418,200
-Linear,Linear,experimental,False,4,0.9,0.66,2.1748653096663464,0.9342553462922274,0.525,2.745378335374161,200
-Linear,Linear,experimental,False,4,0.95,0.7633333333333333,2.5915118575455782,0.9342553462922274,0.655,3.1210909517446286,200
-Linear,Linear,experimental,False,6,0.9,0.8883333333333333,2.217594064950379,0.5525144231877022,0.875,2.791344195057897,200
-Linear,Linear,experimental,False,6,0.95,0.9366666666666668,2.642426309803645,0.5525144231877022,0.92,3.170576857891774,200
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-Linear,Linear,observational,True,4,0.9,0.7633333333333333,3.124012529892269,0.9973634763359689,0.72,3.880323357977367,200
-Linear,Linear,observational,True,4,0.95,0.84,3.7224905277370017,0.9973634763359689,0.81,4.430132897406105,200
-Linear,Linear,observational,True,6,0.9,0.8966666666666667,2.823741863032981,0.7474950667730065,0.88,3.5108185024043683,200
-Linear,Linear,observational,True,6,0.95,0.9466666666666668,3.3646959598709376,0.7474950667730065,0.945,4.0073726417113,200
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+Linear,Linear,observational,True,1,0.95,0.9486666666666667,0.7088946094940543,0.1440501200808088,0.936,0.8623401634425215,1000
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diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
index 0e83932..2f67ace 100644
--- a/results/did/did_multi_metadata.csv
+++ b/results/did/did_multi_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.10.dev0,DIDMultiCoverageSimulation,2025-03-19 17:35,27.171297589937847,3.12.9,scripts/did/multi_config.yml
+0.10.dev0,DIDMultiCoverageSimulation,2025-03-21 13:02,3.9300304452578225,3.12.9,scripts/did/multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
index 0e8a662..7182d05 100644
--- a/results/did/did_multi_time.csv
+++ b/results/did/did_multi_time.csv
@@ -1,25 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.85,0.49360461245245374,0.13317137989658068,0.805,0.6319610870324371,200
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-Linear,Linear,observational,False,6,0.9,0.8966666666666667,2.8422258436545302,0.7656607981855843,0.9,3.235109337869692,200
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-Linear,Linear,observational,True,4,0.9,0.7016666666666667,3.135225381339414,1.0623341589035538,0.66,3.525109938704791,200
-Linear,Linear,observational,True,4,0.95,0.7783333333333333,3.7358514643214518,1.0623341589035538,0.77,4.10980058127257,200
-Linear,Linear,observational,True,6,0.9,0.8816666666666667,2.6295968848181595,0.7121484510464997,0.87,3.011021831651718,200
-Linear,Linear,observational,True,6,0.95,0.935,3.1333579497006316,0.7121484510464997,0.95,3.4966663250213488,200
+Linear,Linear,experimental,False,1,0.9,0.862,0.4928810792988786,0.13124379587670076,0.839,0.6315410055040674,1000
+Linear,Linear,experimental,False,1,0.95,0.9253333333333333,0.5873040301327268,0.13124379587670076,0.905,0.7136054232732442,1000
+Linear,Linear,experimental,False,4,0.9,0.5016666666666667,1.9657848813119838,1.0502850008012623,0.473,2.259694817907404,1000
+Linear,Linear,experimental,False,4,0.95,0.5956666666666667,2.34237716085755,1.0502850008012623,0.576,2.6247319895995456,1000
+Linear,Linear,experimental,False,6,0.9,0.915,1.9600200144024984,0.46223102289234697,0.913,2.2566500717930382,1000
+Linear,Linear,experimental,False,6,0.95,0.9573333333333334,2.3355078982477218,0.46223102289234697,0.955,2.6243904341111817,1000
+Linear,Linear,experimental,True,1,0.9,0.86,0.49279866312558346,0.13118024657417612,0.835,0.6314273700892165,1000
+Linear,Linear,experimental,True,1,0.95,0.9256666666666666,0.5872058252050936,0.13118024657417612,0.903,0.7135421188498914,1000
+Linear,Linear,experimental,True,4,0.9,0.4956666666666667,1.9645783402752341,1.048403876833638,0.482,2.257895538601638,1000
+Linear,Linear,experimental,True,4,0.95,0.6013333333333334,2.340939478537888,1.048403876833638,0.576,2.623735792365416,1000
+Linear,Linear,experimental,True,6,0.9,0.916,1.9596368430072597,0.4616670580420602,0.917,2.258846499216311,1000
+Linear,Linear,experimental,True,6,0.95,0.9583333333333334,2.335051321369227,0.4616670580420602,0.957,2.619661989243463,1000
+Linear,Linear,observational,False,1,0.9,0.9046666666666666,0.6029754636773904,0.14420577130973078,0.91,0.7706892509169176,1000
+Linear,Linear,observational,False,1,0.95,0.9546666666666667,0.7184895804737107,0.14420577130973078,0.954,0.8719065946335489,1000
+Linear,Linear,observational,False,4,0.9,0.682,3.3042482077466073,1.1219461153182708,0.651,3.7179171164272695,1000
+Linear,Linear,observational,False,4,0.95,0.7726666666666666,3.937254584267904,1.1219461153182708,0.738,4.337439826876367,1000
+Linear,Linear,observational,False,6,0.9,0.914,2.5295434432500326,0.631825915195643,0.917,2.9157792155421123,1000
+Linear,Linear,observational,False,6,0.95,0.9626666666666667,3.014136920674323,0.631825915195643,0.967,3.386519653775383,1000
+Linear,Linear,observational,True,1,0.9,0.8916666666666666,0.5764292428073284,0.14270494869961284,0.893,0.7373182480398929,1000
+Linear,Linear,observational,True,1,0.95,0.9456666666666667,0.6868578073004362,0.14270494869961284,0.943,0.8337504767047077,1000
+Linear,Linear,observational,True,4,0.9,0.6606666666666666,3.110533543222582,1.1123478588300635,0.623,3.5061830916939076,1000
+Linear,Linear,observational,True,4,0.95,0.7673333333333334,3.7064293244859585,1.1123478588300635,0.727,4.084196858643447,1000
+Linear,Linear,observational,True,6,0.9,0.9073333333333333,2.4168915264549797,0.611462867914882,0.895,2.7838277996862724,1000
+Linear,Linear,observational,True,6,0.95,0.9533333333333334,2.879903882493947,0.611462867914882,0.953,3.2317290279843696,1000
diff --git a/results/did/multi_config.yml b/results/did/multi_config.yml
index b53271c..f7579ad 100644
--- a/results/did/multi_config.yml
+++ b/results/did/multi_config.yml
@@ -46,5 +46,6 @@ dml_parameters:
   - experimental
 simulation_parameters:
   max_runtime: 19800
+  n_jobs: -2
   random_seed: 42
-  repetitions: 200
+  repetitions: 1000
diff --git a/scripts/did/did_pa_multi_coverage.py b/scripts/did/did_pa_multi_coverage.py
index 7d2a191..af28d1c 100644
--- a/scripts/did/did_pa_multi_coverage.py
+++ b/scripts/did/did_pa_multi_coverage.py
@@ -4,7 +4,7 @@
 # Create and run simulation with config file
 sim = DIDMultiCoverageSimulation(
     config_file="scripts/did/multi_config.yml",
-    log_level="INFO",
+    log_level="DEBUG",
     log_file="logs/did/multi_sim.log"
 )
 sim.run_simulation()
diff --git a/scripts/did/multi_config.yml b/scripts/did/multi_config.yml
index de4d854..396085c 100644
--- a/scripts/did/multi_config.yml
+++ b/scripts/did/multi_config.yml
@@ -1,9 +1,10 @@
 # Simulation parameters for DID Multi Coverage
 
 simulation_parameters:
-  repetitions: 200
+  repetitions: 1000
   max_runtime: 19800 # 5.5 hours in seconds
   random_seed: 42
+  n_jobs: -2
 
 dgp_parameters:
   DGP: [1, 4, 6] # Different DGP specifications

From 0abb329e7a7ceae299bf63acba1ab9491bd4308e Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 13:09:16 +0100
Subject: [PATCH 30/60] formatting

---
 monte-cover/src/montecover/base.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
index c312e0a..957f0fe 100644
--- a/monte-cover/src/montecover/base.py
+++ b/monte-cover/src/montecover/base.py
@@ -113,7 +113,9 @@ def run_simulation(self, n_jobs=None):
             self.logger.info(f"Starting parallel execution with n_jobs={n_jobs}")
             with parallel_backend("loky", inner_max_num_threads=1):
                 results = Parallel(n_jobs=n_jobs, verbose=10)(
-                    delayed(self._process_repetition)(i_rep) for i_rep in range(self.repetitions) if not self._stop_simulation()
+                    delayed(self._process_repetition)(i_rep)
+                    for i_rep in range(self.repetitions)
+                    if not self._stop_simulation()
                 )
 
             # Process results from parallel execution

From 06ab4538e913ae4e0bdd8d29ae564defaebc524b Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 13:46:56 +0100
Subject: [PATCH 31/60] rename did multi files for consistency

---
 monte-cover/src/montecover/did/__init__.py    |  2 +-
 .../did/{did_multi.py => did_pa_multi.py}     |  0
 results/did/did_multi_detailed.csv            | 48 ++++++++---------
 results/did/did_multi_eventstudy.csv          | 48 ++++++++---------
 results/did/did_multi_group.csv               | 48 ++++++++---------
 results/did/did_multi_metadata.csv            |  2 +-
 results/did/did_multi_time.csv                | 48 ++++++++---------
 results/did/multi_config.yml                  | 51 -------------------
 ...d_pa_multi_coverage.py => did_pa_multi.py} |  8 +--
 ...lti_config.yml => did_pa_multi_config.yml} |  0
 10 files changed, 102 insertions(+), 153 deletions(-)
 rename monte-cover/src/montecover/did/{did_multi.py => did_pa_multi.py} (100%)
 delete mode 100644 results/did/multi_config.yml
 rename scripts/did/{did_pa_multi_coverage.py => did_pa_multi.py} (53%)
 rename scripts/did/{multi_config.yml => did_pa_multi_config.yml} (100%)

diff --git a/monte-cover/src/montecover/did/__init__.py b/monte-cover/src/montecover/did/__init__.py
index 24967e6..5aecac3 100644
--- a/monte-cover/src/montecover/did/__init__.py
+++ b/monte-cover/src/montecover/did/__init__.py
@@ -1,5 +1,5 @@
 """Monte Carlo coverage simulations for DiD."""
 
-from montecover.did.did_multi import DIDMultiCoverageSimulation
+from montecover.did.did_pa_multi import DIDMultiCoverageSimulation
 
 __all__ = ["DIDMultiCoverageSimulation"]
diff --git a/monte-cover/src/montecover/did/did_multi.py b/monte-cover/src/montecover/did/did_pa_multi.py
similarity index 100%
rename from monte-cover/src/montecover/did/did_multi.py
rename to monte-cover/src/montecover/did/did_pa_multi.py
diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
index 3d0b81a..b985bdb 100644
--- a/results/did/did_multi_detailed.csv
+++ b/results/did/did_multi_detailed.csv
@@ -1,25 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.8813333333333334,0.5961686505266492,0.15102133299318632,0.834,0.9296789699097547,1000
-Linear,Linear,experimental,False,1,0.95,0.9350833333333334,0.7103787623398975,0.15102133299318632,0.908,1.0206042134000024,1000
-Linear,Linear,experimental,False,4,0.9,0.6619166666666666,1.986651429023609,0.8637379841284726,0.52,2.882026229739649,1000
-Linear,Linear,experimental,False,4,0.95,0.7520833333333333,2.3672411860366602,0.8637379841284726,0.617,3.2128015018838694,1000
-Linear,Linear,experimental,False,6,0.9,0.902,2.00497022725443,0.4810970023674208,0.911,2.9031760219044904,1000
-Linear,Linear,experimental,False,6,0.95,0.9528333333333334,2.3890693804633027,0.4810970023674208,0.951,3.238600319213653,1000
-Linear,Linear,experimental,True,1,0.9,0.8800833333333333,0.5960959285920073,0.15123045002955704,0.833,0.928366902428871,1000
-Linear,Linear,experimental,True,1,0.95,0.9353333333333333,0.7102921088100949,0.15123045002955704,0.913,1.0197692316323934,1000
-Linear,Linear,experimental,True,4,0.9,0.6590833333333334,1.9857772419115973,0.8626296646616975,0.52,2.8815675975045956,1000
-Linear,Linear,experimental,True,4,0.95,0.7531666666666667,2.3661995278445764,0.8626296646616975,0.619,3.2112254886896605,1000
-Linear,Linear,experimental,True,6,0.9,0.9019166666666666,2.004527154284187,0.4808213402748324,0.905,2.9011439332421705,1000
-Linear,Linear,experimental,True,6,0.95,0.952,2.3885414264557423,0.4808213402748324,0.949,3.237049999080413,1000
-Linear,Linear,observational,False,1,0.9,0.90575,0.6906210074073058,0.16339132921843413,0.897,1.0727694533982786,1000
-Linear,Linear,observational,False,1,0.95,0.9518333333333334,0.8229256873110352,0.16339132921843413,0.946,1.1792381527650921,1000
-Linear,Linear,observational,False,4,0.9,0.7795,2.9571361297998364,0.9287818108191471,0.691,4.19740184270945,1000
-Linear,Linear,observational,False,4,0.95,0.8571666666666666,3.5236450324955486,0.9287818108191471,0.78,4.697698545475343,1000
-Linear,Linear,observational,False,6,0.9,0.9076666666666666,2.5544833881976365,0.6416394592068319,0.907,3.6632944671996808,1000
-Linear,Linear,observational,False,6,0.95,0.9550833333333334,3.0438546980332184,0.6416394592068319,0.958,4.0959649390037125,1000
-Linear,Linear,observational,True,1,0.9,0.8973333333333333,0.668426359641987,0.16249117038954014,0.895,1.038888800002171,1000
-Linear,Linear,observational,True,1,0.95,0.9465833333333333,0.7964791333096312,0.16249117038954014,0.939,1.1417719060066087,1000
-Linear,Linear,observational,True,4,0.9,0.7675,2.823122727518332,0.9146880810616262,0.686,4.025190417054882,1000
-Linear,Linear,observational,True,4,0.95,0.8508333333333333,3.3639582144020532,0.9146880810616262,0.768,4.499697598146944,1000
-Linear,Linear,observational,True,6,0.9,0.89775,2.4371258648326704,0.6190705910995143,0.893,3.5036401350107074,1000
-Linear,Linear,observational,True,6,0.95,0.9521666666666666,2.9040145837876374,0.6190705910995143,0.955,3.9130910221754434,1000
+Linear,Linear,experimental,False,1,0.9,0.8770833333333333,0.5956631078920439,0.15393126826790132,0.846,0.9280642120939564,1000
+Linear,Linear,experimental,False,1,0.95,0.9353333333333333,0.70977637113606,0.15393126826790132,0.907,1.01868658603788,1000
+Linear,Linear,experimental,False,4,0.9,0.6671666666666666,1.9827195149925345,0.8550657449695865,0.505,2.8772921242320737,1000
+Linear,Linear,experimental,False,4,0.95,0.758,2.362556021493784,0.8550657449695865,0.628,3.2083215653145722,1000
+Linear,Linear,experimental,False,6,0.9,0.9025,2.00173937868972,0.49027975353015657,0.903,2.8952108652661397,1000
+Linear,Linear,experimental,False,6,0.95,0.9499166666666666,2.385219586948198,0.49027975353015657,0.939,3.230482678959989,1000
+Linear,Linear,experimental,True,1,0.9,0.8771666666666667,0.5956064011739037,0.15426338756081115,0.842,0.9278381417397478,1000
+Linear,Linear,experimental,True,1,0.95,0.9339166666666666,0.7097088009137527,0.15426338756081115,0.912,1.0187258089279716,1000
+Linear,Linear,experimental,True,4,0.9,0.6675,1.9828114892881032,0.8559251553294747,0.507,2.8761724678009353,1000
+Linear,Linear,experimental,True,4,0.95,0.7571666666666667,2.362665615626578,0.8559251553294747,0.618,3.204786768191425,1000
+Linear,Linear,experimental,True,6,0.9,0.9008333333333334,2.002017704071254,0.4900985674785569,0.904,2.897432562708396,1000
+Linear,Linear,experimental,True,6,0.95,0.9494166666666666,2.3855512320957373,0.4900985674785569,0.945,3.231599891589011,1000
+Linear,Linear,observational,False,1,0.9,0.9048333333333334,0.6855202939670088,0.16429464571110677,0.905,1.0652155650078814,1000
+Linear,Linear,observational,False,1,0.95,0.9518333333333334,0.8168478123715063,0.16429464571110677,0.951,1.1708094409885172,1000
+Linear,Linear,observational,False,4,0.9,0.7851666666666667,2.7948549075952385,0.8941603312052668,0.689,4.003170404405238,1000
+Linear,Linear,observational,False,4,0.95,0.8580833333333334,3.3302750294286816,0.8941603312052668,0.786,4.4764083966934205,1000
+Linear,Linear,observational,False,6,0.9,0.8918333333333334,2.540792582093424,0.6659145487088738,0.893,3.64735417288717,1000
+Linear,Linear,observational,False,6,0.95,0.9475,3.0275410963583322,0.6659145487088738,0.941,4.076810932574564,1000
+Linear,Linear,observational,True,1,0.9,0.8975,0.6634200132755311,0.16175894761877385,0.888,1.0319404210431198,1000
+Linear,Linear,observational,True,1,0.95,0.94725,0.790513703674064,0.16175894761877385,0.94,1.1332864016074393,1000
+Linear,Linear,observational,True,4,0.9,0.7758333333333334,2.7290002317619733,0.8973641897321528,0.674,3.912420538282281,1000
+Linear,Linear,observational,True,4,0.95,0.8505833333333334,3.2518043432035615,0.8973641897321528,0.792,4.369245108445732,1000
+Linear,Linear,observational,True,6,0.9,0.8846666666666666,2.4411244723625476,0.6398322462292214,0.892,3.513044851639997,1000
+Linear,Linear,observational,True,6,0.95,0.9423333333333334,2.9087792185359564,0.6398322462292214,0.945,3.923376128802193,1000
diff --git a/results/did/did_multi_eventstudy.csv b/results/did/did_multi_eventstudy.csv
index 78a9bb7..909cde3 100644
--- a/results/did/did_multi_eventstudy.csv
+++ b/results/did/did_multi_eventstudy.csv
@@ -1,25 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.8746666666666666,0.42536022351351016,0.11129964461197367,0.856,0.6065601425726624,1000
-Linear,Linear,experimental,False,1,0.95,0.9343333333333333,0.5068479680392762,0.11129964461197367,0.923,0.6740692167988962,1000
-Linear,Linear,experimental,False,4,0.9,0.6018333333333333,1.9789774969843743,0.9612815234569009,0.504,2.557725621904857,1000
-Linear,Linear,experimental,False,4,0.95,0.6915,2.3580971320185626,0.9612815234569009,0.606,2.9077454096108863,1000
-Linear,Linear,experimental,False,6,0.9,0.9026666666666666,2.0021783896522174,0.475645152957695,0.913,2.5813810385024185,1000
-Linear,Linear,experimental,False,6,0.95,0.9546666666666667,2.3857427007749936,0.475645152957695,0.954,2.939126540032628,1000
-Linear,Linear,experimental,True,1,0.9,0.8736666666666666,0.4252780101030476,0.11135661295209692,0.855,0.6067206852239997,1000
-Linear,Linear,experimental,True,1,0.95,0.9338333333333334,0.50675000471846,0.11135661295209692,0.919,0.6737727543313963,1000
-Linear,Linear,experimental,True,4,0.9,0.604,1.9778347169116364,0.9610017220191109,0.506,2.5570866190887442,1000
-Linear,Linear,experimental,True,4,0.95,0.69,2.356735425573614,0.9610017220191109,0.614,2.906700104748762,1000
-Linear,Linear,experimental,True,6,0.9,0.9028333333333334,2.001368819203873,0.47488976645105485,0.91,2.5788107797213202,1000
-Linear,Linear,experimental,True,6,0.95,0.9543333333333334,2.3847780380866523,0.47488976645105485,0.954,2.9346744455419307,1000
-Linear,Linear,observational,False,1,0.9,0.9048333333333334,0.4936490463630555,0.11703447731264664,0.911,0.7026803919052239,1000
-Linear,Linear,observational,False,1,0.95,0.9523333333333334,0.5882191193312043,0.11703447731264664,0.953,0.7817258159201687,1000
-Linear,Linear,observational,False,4,0.9,0.7483333333333334,3.110852841424021,1.0162956359155522,0.691,3.927457336373739,1000
-Linear,Linear,observational,False,4,0.95,0.8321666666666666,3.7068097917596994,1.0162956359155522,0.775,4.489588602429944,1000
-Linear,Linear,observational,False,6,0.9,0.908,2.6065175455108682,0.657901837298179,0.912,3.319528013865773,1000
-Linear,Linear,observational,False,6,0.95,0.9611666666666666,3.1058572207068265,0.657901837298179,0.96,3.78925559700768,1000
-Linear,Linear,observational,True,1,0.9,0.8983333333333333,0.478045989951027,0.1165385646041097,0.898,0.6810014878305387,1000
-Linear,Linear,observational,True,1,0.95,0.9476666666666667,0.5696269308742887,0.1165385646041097,0.951,0.7570193996194396,1000
-Linear,Linear,observational,True,4,0.9,0.7318333333333333,2.961887231450272,1.0118391967276548,0.664,3.755668505865932,1000
-Linear,Linear,observational,True,4,0.95,0.8186666666666667,3.5293063192928438,1.0118391967276548,0.771,4.287491165909032,1000
-Linear,Linear,observational,True,6,0.9,0.8983333333333333,2.4694251965466845,0.6253200792538618,0.908,3.1600238226116533,1000
-Linear,Linear,observational,True,6,0.95,0.9541666666666666,2.9425016113546496,0.6253200792538618,0.955,3.596198019745218,1000
+Linear,Linear,experimental,False,1,0.9,0.8676666666666666,0.42543423226976995,0.11378217794214893,0.82,0.607026998501089,1000
+Linear,Linear,experimental,False,1,0.95,0.921,0.5069361549116113,0.11378217794214893,0.893,0.6744173882852083,1000
+Linear,Linear,experimental,False,4,0.9,0.6021666666666666,1.9754395142835157,0.9518163003680035,0.488,2.5538078783006415,1000
+Linear,Linear,experimental,False,4,0.95,0.7053333333333334,2.353881365607505,0.9518163003680035,0.602,2.9021513463156756,1000
+Linear,Linear,experimental,False,6,0.9,0.8995,2.001327059411559,0.49627387221768626,0.899,2.5768571826106514,1000
+Linear,Linear,experimental,False,6,0.95,0.949,2.3847282782249866,0.49627387221768626,0.942,2.9364211171912067,1000
+Linear,Linear,experimental,True,1,0.9,0.8666666666666666,0.42531823168334737,0.1141016026224358,0.814,0.6067463378989032,1000
+Linear,Linear,experimental,True,1,0.95,0.9231666666666666,0.5067979316874602,0.1141016026224358,0.889,0.6733443239100646,1000
+Linear,Linear,experimental,True,4,0.9,0.5973333333333334,1.975365775525183,0.9534736795550611,0.491,2.5532803279562994,1000
+Linear,Linear,experimental,True,4,0.95,0.7055,2.353793500457543,0.9534736795550611,0.61,2.905573717129059,1000
+Linear,Linear,experimental,True,6,0.9,0.8978333333333334,2.0019957931284336,0.4959848232257912,0.9,2.579876093556324,1000
+Linear,Linear,experimental,True,6,0.95,0.9478333333333334,2.385525123597028,0.4959848232257912,0.944,2.9374235003380655,1000
+Linear,Linear,observational,False,1,0.9,0.9013333333333333,0.4873576971767433,0.11751015177996685,0.898,0.693580479033222,1000
+Linear,Linear,observational,False,1,0.95,0.9505,0.5807225143948788,0.11751015177996685,0.944,0.7712324745986096,1000
+Linear,Linear,observational,False,4,0.9,0.762,2.9253154914773845,0.972649026341708,0.692,3.7241194943689173,1000
+Linear,Linear,observational,False,4,0.95,0.8396666666666667,3.485728403286012,0.972649026341708,0.787,4.249241903518029,1000
+Linear,Linear,observational,False,6,0.9,0.8926666666666666,2.596496390444534,0.6912487923777589,0.89,3.308229651637506,1000
+Linear,Linear,observational,False,6,0.95,0.9455,3.0939162779434826,0.6912487923777589,0.948,3.7769009967220812,1000
+Linear,Linear,observational,True,1,0.9,0.8956666666666666,0.4727299379079462,0.11588912406999206,0.894,0.6735775257798401,1000
+Linear,Linear,observational,True,1,0.95,0.944,0.5632924641632966,0.11588912406999206,0.938,0.7483724833065163,1000
+Linear,Linear,observational,True,4,0.9,0.7468333333333333,2.8437612590899457,0.9776121628787491,0.682,3.627947639783714,1000
+Linear,Linear,observational,True,4,0.95,0.8328333333333334,3.3885505415923616,0.9776121628787491,0.775,4.133831167481912,1000
+Linear,Linear,observational,True,6,0.9,0.8851666666666667,2.4823044000101313,0.660809427159733,0.894,3.171860461977204,1000
+Linear,Linear,observational,True,6,0.95,0.9428333333333334,2.957848128834568,0.660809427159733,0.945,3.6158624454294848,1000
diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
index d9fa94b..ab64a5f 100644
--- a/results/did/did_multi_group.csv
+++ b/results/did/did_multi_group.csv
@@ -1,25 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.876,0.5329948975220807,0.13579236732447947,0.846,0.683878514526821,1000
-Linear,Linear,experimental,False,1,0.95,0.9296666666666666,0.6351025926987942,0.13579236732447947,0.917,0.7732276242829601,1000
-Linear,Linear,experimental,False,4,0.9,0.658,2.1938722973937375,0.9674173487082218,0.525,2.767821034152252,1000
-Linear,Linear,experimental,False,4,0.95,0.746,2.614160080335666,0.9674173487082218,0.619,3.1440243876915375,1000
-Linear,Linear,experimental,False,6,0.9,0.9006666666666666,2.211125648458694,0.5338317649415134,0.919,2.783739324710087,1000
-Linear,Linear,experimental,False,6,0.95,0.9543333333333334,2.6347187161594587,0.5338317649415134,0.957,3.1662952947465888,1000
-Linear,Linear,experimental,True,1,0.9,0.8766666666666666,0.5328699306003187,0.1358281841054489,0.848,0.6847832796423149,1000
-Linear,Linear,experimental,True,1,0.95,0.9263333333333333,0.6349536854271081,0.1358281841054489,0.911,0.7727182150404185,1000
-Linear,Linear,experimental,True,4,0.9,0.6586666666666666,2.1921756345553276,0.9652578245005206,0.525,2.7628627454822605,1000
-Linear,Linear,experimental,True,4,0.95,0.7473333333333334,2.6121383818679704,0.9652578245005206,0.624,3.144606886076096,1000
-Linear,Linear,experimental,True,6,0.9,0.8993333333333333,2.2104603719936415,0.5344678450501394,0.912,2.784794352421448,1000
-Linear,Linear,experimental,True,6,0.95,0.954,2.6339259903570538,0.5344678450501394,0.957,3.164126890302782,1000
-Linear,Linear,observational,False,1,0.9,0.9026666666666666,0.6123404760096205,0.1438612793224918,0.904,0.7860078542582829,1000
-Linear,Linear,observational,False,1,0.95,0.9546666666666667,0.7296486809463548,0.1438612793224918,0.952,0.8872672286776676,1000
-Linear,Linear,observational,False,4,0.9,0.781,3.3074883394733083,1.0352240102334336,0.719,4.086258476471432,1000
-Linear,Linear,observational,False,4,0.95,0.8563333333333334,3.9411154393527843,1.0352240102334336,0.813,4.663160227096469,1000
-Linear,Linear,observational,False,6,0.9,0.899,2.789010379110977,0.6998566663431153,0.912,3.4660289192044,1000
-Linear,Linear,observational,False,6,0.95,0.9553333333333334,3.323310844197804,0.6998566663431153,0.96,3.9528394153130857,1000
-Linear,Linear,observational,True,1,0.9,0.9023333333333333,0.5949231101949434,0.1440501200808088,0.897,0.763308057016272,1000
-Linear,Linear,observational,True,1,0.95,0.9486666666666667,0.7088946094940543,0.1440501200808088,0.936,0.8623401634425215,1000
-Linear,Linear,observational,True,4,0.9,0.7643333333333334,3.142810509409861,1.0233709921822023,0.706,3.8984668952364427,1000
-Linear,Linear,observational,True,4,0.95,0.8463333333333334,3.7448897018841176,1.0233709921822023,0.804,4.447829548666937,1000
-Linear,Linear,observational,True,6,0.9,0.8976666666666666,2.6701046408964046,0.6759660173055291,0.902,3.329342240927625,1000
-Linear,Linear,observational,True,6,0.95,0.9526666666666667,3.181625918173329,0.6759660173055291,0.953,3.7967419653105576,1000
+Linear,Linear,experimental,False,1,0.9,0.865,0.5332689974285741,0.14125883992697605,0.84,0.684967458838645,1000
+Linear,Linear,experimental,False,1,0.95,0.923,0.6354292028822721,0.14125883992697605,0.898,0.7737658489393863,1000
+Linear,Linear,experimental,False,4,0.9,0.6613333333333332,2.187885379540459,0.9604906586192218,0.498,2.7575340571302047,1000
+Linear,Linear,experimental,False,4,0.95,0.7556666666666666,2.607026227706741,0.9604906586192218,0.602,3.1362004034484614,1000
+Linear,Linear,experimental,False,6,0.9,0.8976666666666666,2.2072703099683895,0.5422311709957129,0.907,2.7789385342819997,1000
+Linear,Linear,experimental,False,6,0.95,0.9506666666666667,2.6301247970013075,0.5422311709957129,0.941,3.1616330454003854,1000
+Linear,Linear,experimental,True,1,0.9,0.8643333333333334,0.5331656474907569,0.1416028173409448,0.837,0.6848091387598605,1000
+Linear,Linear,experimental,True,1,0.95,0.9223333333333333,0.6353060538356899,0.1416028173409448,0.904,0.7729755854524409,1000
+Linear,Linear,experimental,True,4,0.9,0.6636666666666666,2.1871578881758738,0.9618357917015027,0.495,2.757365097655949,1000
+Linear,Linear,experimental,True,4,0.95,0.7556666666666666,2.606159368279076,0.9618357917015027,0.604,3.1339986032881795,1000
+Linear,Linear,experimental,True,6,0.9,0.8983333333333333,2.2070772542069657,0.5424515866558068,0.899,2.779655081198074,1000
+Linear,Linear,experimental,True,6,0.95,0.9513333333333334,2.629894756872995,0.5424515866558068,0.942,3.16003069657915,1000
+Linear,Linear,observational,False,1,0.9,0.902,0.6106049709052258,0.14629034271149396,0.89,0.7831902414227334,1000
+Linear,Linear,observational,False,1,0.95,0.947,0.727580699064038,0.14629034271149396,0.942,0.8844844476393456,1000
+Linear,Linear,observational,False,4,0.9,0.7813333333333333,3.0907797972605278,0.9943909423280353,0.706,3.8523704910840313,1000
+Linear,Linear,observational,False,4,0.95,0.8523333333333334,3.682891284376495,0.9943909423280353,0.809,4.39501177360628,1000
+Linear,Linear,observational,False,6,0.9,0.89,2.7865039885472247,0.7301188763670151,0.894,3.468312373428792,1000
+Linear,Linear,observational,False,6,0.95,0.95,3.3203242956346655,0.7301188763670151,0.945,3.9541003782057595,1000
+Linear,Linear,observational,True,1,0.9,0.8946666666666666,0.5896388541244254,0.14371827951522767,0.878,0.7568642372370822,1000
+Linear,Linear,observational,True,1,0.95,0.9423333333333334,0.7025980300212064,0.14371827951522767,0.936,0.8549636526386375,1000
+Linear,Linear,observational,True,4,0.9,0.777,3.0325048545897997,1.0070643044350587,0.698,3.7834488519755434,1000
+Linear,Linear,observational,True,4,0.95,0.8456666666666667,3.6134524072847696,1.0070643044350587,0.784,4.309691278217546,1000
+Linear,Linear,observational,True,6,0.9,0.8863333333333334,2.674147344485724,0.6969076659422232,0.89,3.3341905763684103,1000
+Linear,Linear,observational,True,6,0.95,0.9453333333333334,3.1864430966172987,0.6969076659422232,0.943,3.804017709516315,1000
diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
index 2f67ace..4124b94 100644
--- a/results/did/did_multi_metadata.csv
+++ b/results/did/did_multi_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.10.dev0,DIDMultiCoverageSimulation,2025-03-21 13:02,3.9300304452578225,3.12.9,scripts/did/multi_config.yml
+0.10.dev0,DIDMultiCoverageSimulation,2025-03-21 13:46,3.4916115164756776,3.12.9,scripts/did/did_pa_multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
index 7182d05..06c3c22 100644
--- a/results/did/did_multi_time.csv
+++ b/results/did/did_multi_time.csv
@@ -1,25 +1,25 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.862,0.4928810792988786,0.13124379587670076,0.839,0.6315410055040674,1000
-Linear,Linear,experimental,False,1,0.95,0.9253333333333333,0.5873040301327268,0.13124379587670076,0.905,0.7136054232732442,1000
-Linear,Linear,experimental,False,4,0.9,0.5016666666666667,1.9657848813119838,1.0502850008012623,0.473,2.259694817907404,1000
-Linear,Linear,experimental,False,4,0.95,0.5956666666666667,2.34237716085755,1.0502850008012623,0.576,2.6247319895995456,1000
-Linear,Linear,experimental,False,6,0.9,0.915,1.9600200144024984,0.46223102289234697,0.913,2.2566500717930382,1000
-Linear,Linear,experimental,False,6,0.95,0.9573333333333334,2.3355078982477218,0.46223102289234697,0.955,2.6243904341111817,1000
-Linear,Linear,experimental,True,1,0.9,0.86,0.49279866312558346,0.13118024657417612,0.835,0.6314273700892165,1000
-Linear,Linear,experimental,True,1,0.95,0.9256666666666666,0.5872058252050936,0.13118024657417612,0.903,0.7135421188498914,1000
-Linear,Linear,experimental,True,4,0.9,0.4956666666666667,1.9645783402752341,1.048403876833638,0.482,2.257895538601638,1000
-Linear,Linear,experimental,True,4,0.95,0.6013333333333334,2.340939478537888,1.048403876833638,0.576,2.623735792365416,1000
-Linear,Linear,experimental,True,6,0.9,0.916,1.9596368430072597,0.4616670580420602,0.917,2.258846499216311,1000
-Linear,Linear,experimental,True,6,0.95,0.9583333333333334,2.335051321369227,0.4616670580420602,0.957,2.619661989243463,1000
-Linear,Linear,observational,False,1,0.9,0.9046666666666666,0.6029754636773904,0.14420577130973078,0.91,0.7706892509169176,1000
-Linear,Linear,observational,False,1,0.95,0.9546666666666667,0.7184895804737107,0.14420577130973078,0.954,0.8719065946335489,1000
-Linear,Linear,observational,False,4,0.9,0.682,3.3042482077466073,1.1219461153182708,0.651,3.7179171164272695,1000
-Linear,Linear,observational,False,4,0.95,0.7726666666666666,3.937254584267904,1.1219461153182708,0.738,4.337439826876367,1000
-Linear,Linear,observational,False,6,0.9,0.914,2.5295434432500326,0.631825915195643,0.917,2.9157792155421123,1000
-Linear,Linear,observational,False,6,0.95,0.9626666666666667,3.014136920674323,0.631825915195643,0.967,3.386519653775383,1000
-Linear,Linear,observational,True,1,0.9,0.8916666666666666,0.5764292428073284,0.14270494869961284,0.893,0.7373182480398929,1000
-Linear,Linear,observational,True,1,0.95,0.9456666666666667,0.6868578073004362,0.14270494869961284,0.943,0.8337504767047077,1000
-Linear,Linear,observational,True,4,0.9,0.6606666666666666,3.110533543222582,1.1123478588300635,0.623,3.5061830916939076,1000
-Linear,Linear,observational,True,4,0.95,0.7673333333333334,3.7064293244859585,1.1123478588300635,0.727,4.084196858643447,1000
-Linear,Linear,observational,True,6,0.9,0.9073333333333333,2.4168915264549797,0.611462867914882,0.895,2.7838277996862724,1000
-Linear,Linear,observational,True,6,0.95,0.9533333333333334,2.879903882493947,0.611462867914882,0.953,3.2317290279843696,1000
+Linear,Linear,experimental,False,1,0.9,0.8453333333333334,0.49324862390769003,0.1376326316621036,0.822,0.6316419735152038,1000
+Linear,Linear,experimental,False,1,0.95,0.912,0.5877419865467073,0.1376326316621036,0.891,0.7143067511536322,1000
+Linear,Linear,experimental,False,4,0.9,0.4776666666666667,1.9593068261105524,1.0466452508829949,0.45,2.2507550996198735,1000
+Linear,Linear,experimental,False,4,0.95,0.579,2.3346580819823073,1.0466452508829949,0.556,2.6163918210317183,1000
+Linear,Linear,experimental,False,6,0.9,0.8956666666666666,1.9573896704373563,0.48930237462467674,0.896,2.2565806380862594,1000
+Linear,Linear,experimental,False,6,0.95,0.9463333333333334,2.3323736500969083,0.48930237462467674,0.941,2.62032018987877,1000
+Linear,Linear,experimental,True,1,0.9,0.8463333333333334,0.4931694609078208,0.1378285637524634,0.824,0.6319264464558539,1000
+Linear,Linear,experimental,True,1,0.95,0.9093333333333333,0.587647658014302,0.1378285637524634,0.895,0.7142050985707156,1000
+Linear,Linear,experimental,True,4,0.9,0.473,1.9591517453060092,1.0480526529722811,0.45,2.250777097405993,1000
+Linear,Linear,experimental,True,4,0.95,0.5806666666666667,2.334473291806076,1.0480526529722811,0.55,2.6128556265062666,1000
+Linear,Linear,experimental,True,6,0.9,0.8966666666666666,1.9576188610509668,0.49183824648546365,0.893,2.253956782621074,1000
+Linear,Linear,experimental,True,6,0.95,0.945,2.3326467475573214,0.49183824648546365,0.943,2.6202784765893865,1000
+Linear,Linear,observational,False,1,0.9,0.8953333333333334,0.5968613600047712,0.14788675870016774,0.878,0.7631035273143418,1000
+Linear,Linear,observational,False,1,0.95,0.94,0.7112041765935568,0.14788675870016774,0.945,0.8624624885034761,1000
+Linear,Linear,observational,False,4,0.9,0.6876666666666666,3.0580400071087412,1.0638406360538493,0.653,3.462223480766848,1000
+Linear,Linear,observational,False,4,0.95,0.7736666666666666,3.643879418209516,1.0638406360538493,0.75,4.0319351473266085,1000
+Linear,Linear,observational,False,6,0.9,0.8823333333333334,2.548375931783461,0.6811130775533906,0.871,2.9334540834011715,1000
+Linear,Linear,observational,False,6,0.95,0.9406666666666667,3.036577214850038,0.6811130775533906,0.945,3.4079635812732043,1000
+Linear,Linear,observational,True,1,0.9,0.8806666666666666,0.5725784741025863,0.14380420604106553,0.867,0.7329506080582452,1000
+Linear,Linear,observational,True,1,0.95,0.9413333333333334,0.682269333377637,0.14380420604106553,0.926,0.8286474263809458,1000
+Linear,Linear,observational,True,4,0.9,0.666,2.97181956637082,1.075066363572111,0.635,3.3631545325412926,1000
+Linear,Linear,observational,True,4,0.95,0.7603333333333334,3.5411414263246734,1.075066363572111,0.748,3.9204031440435947,1000
+Linear,Linear,observational,True,6,0.9,0.8823333333333334,2.4376548170067376,0.6493921794587046,0.877,2.808933035095803,1000
+Linear,Linear,observational,True,6,0.95,0.939,2.904644869178221,0.6493921794587046,0.937,3.2552833144907303,1000
diff --git a/results/did/multi_config.yml b/results/did/multi_config.yml
deleted file mode 100644
index f7579ad..0000000
--- a/results/did/multi_config.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-confidence_parameters:
-  level:
-  - 0.95
-  - 0.9
-dgp_parameters:
-  DGP:
-  - 1
-  - 4
-  - 6
-  n_obs:
-  - 500
-dml_parameters:
-  in_sample_normalization:
-  - true
-  - false
-  learners:
-  - ml_g: !!python/tuple
-    - Linear
-    - !!python/object:sklearn.linear_model._base.LinearRegression
-      _sklearn_version: 1.5.2
-      copy_X: true
-      fit_intercept: true
-      n_jobs: null
-      positive: false
-    ml_m: !!python/tuple
-    - Linear
-    - !!python/object:sklearn.linear_model._logistic.LogisticRegression
-      C: 1.0
-      _sklearn_version: 1.5.2
-      class_weight: null
-      dual: false
-      fit_intercept: true
-      intercept_scaling: 1
-      l1_ratio: null
-      max_iter: 100
-      multi_class: deprecated
-      n_jobs: null
-      penalty: l2
-      random_state: null
-      solver: lbfgs
-      tol: 0.0001
-      verbose: 0
-      warm_start: false
-  score:
-  - observational
-  - experimental
-simulation_parameters:
-  max_runtime: 19800
-  n_jobs: -2
-  random_seed: 42
-  repetitions: 1000
diff --git a/scripts/did/did_pa_multi_coverage.py b/scripts/did/did_pa_multi.py
similarity index 53%
rename from scripts/did/did_pa_multi_coverage.py
rename to scripts/did/did_pa_multi.py
index af28d1c..f06a03e 100644
--- a/scripts/did/did_pa_multi_coverage.py
+++ b/scripts/did/did_pa_multi.py
@@ -1,14 +1,14 @@
 
-from montecover.did.did_multi import DIDMultiCoverageSimulation
+from montecover.did import DIDMultiCoverageSimulation
 
 # Create and run simulation with config file
 sim = DIDMultiCoverageSimulation(
-    config_file="scripts/did/multi_config.yml",
+    config_file="scripts/did/did_pa_multi_config.yml",
     log_level="DEBUG",
-    log_file="logs/did/multi_sim.log"
+    log_file="logs/did/did_pa_multi_sim.log"
 )
 sim.run_simulation()
 sim.save_results(output_path="results/did/", file_prefix="did_multi")
 
 # Save config file for reproducibility
-sim.save_config("results/did/multi_config.yml")
+sim.save_config("results/did/did_pa_multi_config.yml")
diff --git a/scripts/did/multi_config.yml b/scripts/did/did_pa_multi_config.yml
similarity index 100%
rename from scripts/did/multi_config.yml
rename to scripts/did/did_pa_multi_config.yml

From 8b664ff2b250d745180c30f20053528f8465153c Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 13:54:49 +0100
Subject: [PATCH 32/60] add did config

---
 results/did/did_pa_multi_config.yml | 51 +++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 results/did/did_pa_multi_config.yml

diff --git a/results/did/did_pa_multi_config.yml b/results/did/did_pa_multi_config.yml
new file mode 100644
index 0000000..f7579ad
--- /dev/null
+++ b/results/did/did_pa_multi_config.yml
@@ -0,0 +1,51 @@
+confidence_parameters:
+  level:
+  - 0.95
+  - 0.9
+dgp_parameters:
+  DGP:
+  - 1
+  - 4
+  - 6
+  n_obs:
+  - 500
+dml_parameters:
+  in_sample_normalization:
+  - true
+  - false
+  learners:
+  - ml_g: !!python/tuple
+    - Linear
+    - !!python/object:sklearn.linear_model._base.LinearRegression
+      _sklearn_version: 1.5.2
+      copy_X: true
+      fit_intercept: true
+      n_jobs: null
+      positive: false
+    ml_m: !!python/tuple
+    - Linear
+    - !!python/object:sklearn.linear_model._logistic.LogisticRegression
+      C: 1.0
+      _sklearn_version: 1.5.2
+      class_weight: null
+      dual: false
+      fit_intercept: true
+      intercept_scaling: 1
+      l1_ratio: null
+      max_iter: 100
+      multi_class: deprecated
+      n_jobs: null
+      penalty: l2
+      random_state: null
+      solver: lbfgs
+      tol: 0.0001
+      verbose: 0
+      warm_start: false
+  score:
+  - observational
+  - experimental
+simulation_parameters:
+  max_runtime: 19800
+  n_jobs: -2
+  random_seed: 42
+  repetitions: 1000

From dc0c6123d6b4db53901d290830f1624d28c8649c Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 13:56:42 +0100
Subject: [PATCH 33/60] first version plr

---
 monte-cover/src/montecover/plm/__init__.py |   5 +
 monte-cover/src/montecover/plm/plr_ate.py  | 137 +++++++++++++++++++++
 scripts/plm/plr_ate.py                     |  14 +++
 scripts/plm/plr_ate_config.yml             |  29 +++++
 4 files changed, 185 insertions(+)
 create mode 100644 monte-cover/src/montecover/plm/__init__.py
 create mode 100644 monte-cover/src/montecover/plm/plr_ate.py
 create mode 100644 scripts/plm/plr_ate.py
 create mode 100644 scripts/plm/plr_ate_config.yml

diff --git a/monte-cover/src/montecover/plm/__init__.py b/monte-cover/src/montecover/plm/__init__.py
new file mode 100644
index 0000000..f861d2b
--- /dev/null
+++ b/monte-cover/src/montecover/plm/__init__.py
@@ -0,0 +1,5 @@
+"""Monte Carlo coverage simulations for PLM."""
+
+from montecover.plm.plr_ate import PLRATECoverageSimulation
+
+__all__ = ["PLRATECoverageSimulation"]
diff --git a/monte-cover/src/montecover/plm/plr_ate.py b/monte-cover/src/montecover/plm/plr_ate.py
new file mode 100644
index 0000000..803c583
--- /dev/null
+++ b/monte-cover/src/montecover/plm/plr_ate.py
@@ -0,0 +1,137 @@
+from typing import Any, Dict, Optional
+
+import doubleml as dml
+from doubleml.datasets import make_plr_CCDDHNR2018
+from lightgbm import LGBMRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LassoCV
+
+from montecover.base import BaseSimulation
+
+
+class PLRATECoverageSimulation(BaseSimulation):
+    """Simulation study for coverage properties of DoubleMLPLR for ATE estimation."""
+
+    def __init__(
+        self,
+        config_file: str,
+        suppress_warnings: bool = True,
+        log_level: str = "INFO",
+        log_file: Optional[str] = None,
+    ):
+        super().__init__(
+            config_file=config_file,
+            suppress_warnings=suppress_warnings,
+            log_level=log_level,
+            log_file=log_file,
+        )
+
+        # Additional results storage for aggregated results
+        self.results_aggregated = []
+
+        # Calculate oracle values
+        self._calculate_oracle_values()
+
+    def _process_config_parameters(self):
+        """Process simulation-specific parameters from config"""
+        # Process ML models in parameter grid
+        assert "learners" in self.dml_parameters, "No learners specified in the config file"
+        for learner in self.dml_parameters["learners"]:
+            assert "ml_g" in learner, "No ml_g specified in the config file"
+            assert "ml_m" in learner, "No ml_m specified in the config file"
+
+            # Convert ml_g strings to actual objects
+            learner["ml_g"] = self._convert_ml_string_to_object(learner["ml_g"][0])
+            learner["ml_m"] = self._convert_ml_string_to_object(learner["ml_m"][0])
+
+    def _convert_ml_string_to_object(self, ml_string):
+        """Convert a string to a machine learning object."""
+        if ml_string == "Lasso":
+            learner = LassoCV()
+        elif ml_string == "Random Forest":
+            learner = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
+        elif ml_string == "LGBM":
+            learner = LGBMRegressor(n_estimators=100, learning_rate=0.05, verbose=-1)
+        else:
+            raise ValueError(f"Unknown learner type: {ml_string}")
+
+        return (ml_string, learner)
+
+    def _calculate_oracle_values(self):
+        """Calculate oracle values for the simulation."""
+        self.logger.info("Calculating oracle values")
+
+        self.oracle_values = dict()
+        self.oracle_values["theta"] = self.dgp_parameters["theta"]
+
+    def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
+        """Run a single repetition with the given parameters."""
+        # Extract parameters
+        learner_g_name, ml_g = dml_params["learners"]["ml_g"]
+        learner_m_name, ml_m = dml_params["learners"]["ml_m"]
+        score = dml_params["score"]
+
+        # Model
+        dml_model = dml.DoubleMLPLR(
+            obj_dml_data=dml_data,
+            ml_l=ml_g,
+            ml_m=ml_m,
+            ml_g=ml_g if score == "IV-type" else None,
+            score=score,
+        )
+        dml_model.fit()
+
+        result = {
+            "coverage": [],
+        }
+        for level in self.confidence_parameters["level"]:
+            level_result = dict()
+            level_result["coverage"] = self._compute_coverage(
+                thetas=dml_model.coef,
+                oracle_thetas=self.oracle_values["theta"],
+                confint=dml_model.confint(level=level),
+                joint_confint=None,
+            )
+
+            # add parameters to the result
+            for res in level_result.values():
+                res.update(
+                    {
+                        "Learner g": learner_g_name,
+                        "Learner m": learner_m_name,
+                        "Score": score,
+                        "level": level,
+                    }
+                )
+            for key, res in level_result.items():
+                result[key].append(res)
+
+        return result
+
+    def summarize_results(self):
+        """Summarize the simulation results."""
+        self.logger.info("Summarizing simulation results")
+
+        # Group by parameter combinations
+        groupby_cols = ["Learner g", "Learner m", "Score", "level"]
+        aggregation_dict = {
+            "Coverage": "mean",
+            "CI Length": "mean",
+            "Bias": "mean",
+        }
+
+        # Aggregate results (possibly multiple result dfs)
+        result_summary = dict()
+        for result_name, result_df in self.results.items():
+            result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index()
+            self.logger.debug(f"Summarized {result_name} results")
+
+        return result_summary
+
+    def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData:
+        """Generate data for the simulation."""
+        data = make_plr_CCDDHNR2018(
+            alpha=dgp_params["theta"], n_obs=dgp_params["n_obs"], dim_x=dgp_params["dim_x"], return_type="DataFrame"
+        )
+        dml_data = dml.DoubleMLData(data, "y", "d")
+        return dml_data
diff --git a/scripts/plm/plr_ate.py b/scripts/plm/plr_ate.py
new file mode 100644
index 0000000..09e2531
--- /dev/null
+++ b/scripts/plm/plr_ate.py
@@ -0,0 +1,14 @@
+
+from montecover.plm import PLRATECoverageSimulation
+
+# Create and run simulation with config file
+sim = PLRATECoverageSimulation(
+    config_file="scripts/plm/plr_ate_config.yml",
+    log_level="INFO",
+    log_file="logs/plm/plr_ate_sim.log"
+)
+sim.run_simulation()
+sim.save_results(output_path="results/plm/", file_prefix="plr_ate")
+
+# Save config file for reproducibility
+sim.save_config("results/plm/plr_ate_config.yml")
diff --git a/scripts/plm/plr_ate_config.yml b/scripts/plm/plr_ate_config.yml
new file mode 100644
index 0000000..479240c
--- /dev/null
+++ b/scripts/plm/plr_ate_config.yml
@@ -0,0 +1,29 @@
+# Simulation parameters for PLR ATE Coverage
+
+simulation_parameters:
+  repetitions: 10
+  max_runtime: 19800 # 5.5 hours in seconds
+  random_seed: 42
+  n_jobs: -2
+
+dgp_parameters:
+  theta: [0.5] # Treatment effect
+  n_obs: [500] # Sample size
+  dim_x: [20] # Number of covariates
+
+dml_parameters:
+  # ML methods for ml_g and ml_m
+  learners:
+    - ml_g: ["Lasso"]
+      ml_m: ["Lasso"]
+    - ml_g: ["Random Forest"]
+      ml_m: ["Random Forest"]
+    - ml_g: ["Lasso"]
+      ml_m: ["Random Forest"]
+    - ml_g: ["Random Forest"]
+      ml_m: ["Lasso"]
+
+  score: ["partialling out", "IV-type"]
+
+confidence_parameters:
+  level: [0.95, 0.90] # Confidence levels
\ No newline at end of file

From f35d661c8a04850bef15541ee64ca0e5942d4a52 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 15:17:47 +0100
Subject: [PATCH 34/60] add plr sim

---
 monte-cover/src/montecover/plm/plr_ate.py |   2 +-
 results/plm/plr_ate_config.yml            | 332 ++++++++++++++++++++++
 results/plm/plr_ate_coverage.csv          |  38 +--
 results/plm/plr_ate_metadata.csv          |   2 +
 scripts/plm/plr_ate_config.yml            |   4 +-
 5 files changed, 359 insertions(+), 19 deletions(-)
 create mode 100644 results/plm/plr_ate_config.yml
 create mode 100644 results/plm/plr_ate_metadata.csv

diff --git a/monte-cover/src/montecover/plm/plr_ate.py b/monte-cover/src/montecover/plm/plr_ate.py
index 803c583..e6119c6 100644
--- a/monte-cover/src/montecover/plm/plr_ate.py
+++ b/monte-cover/src/montecover/plm/plr_ate.py
@@ -51,7 +51,7 @@ def _convert_ml_string_to_object(self, ml_string):
         elif ml_string == "Random Forest":
             learner = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
         elif ml_string == "LGBM":
-            learner = LGBMRegressor(n_estimators=100, learning_rate=0.05, verbose=-1)
+            learner = LGBMRegressor(n_estimators=100, learning_rate=0.05, verbose=-1, n_jobs=1)
         else:
             raise ValueError(f"Unknown learner type: {ml_string}")
 
diff --git a/results/plm/plr_ate_config.yml b/results/plm/plr_ate_config.yml
new file mode 100644
index 0000000..18c7f83
--- /dev/null
+++ b/results/plm/plr_ate_config.yml
@@ -0,0 +1,332 @@
+confidence_parameters:
+  level:
+  - 0.95
+  - 0.9
+dgp_parameters:
+  dim_x:
+  - 20
+  n_obs:
+  - 500
+  theta:
+  - 0.5
+dml_parameters:
+  learners:
+  - ml_g: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+    ml_m: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+  - ml_g: !!python/tuple
+    - Random Forest
+    - !!python/object:sklearn.ensemble._forest.RandomForestRegressor
+      _sklearn_version: 1.5.2
+      bootstrap: true
+      ccp_alpha: 0.0
+      class_weight: null
+      criterion: squared_error
+      estimator: !!python/object:sklearn.tree._classes.DecisionTreeRegressor
+        _sklearn_version: 1.5.2
+        ccp_alpha: 0.0
+        class_weight: null
+        criterion: squared_error
+        max_depth: null
+        max_features: null
+        max_leaf_nodes: null
+        min_impurity_decrease: 0.0
+        min_samples_leaf: 1
+        min_samples_split: 2
+        min_weight_fraction_leaf: 0.0
+        monotonic_cst: null
+        random_state: null
+        splitter: best
+      estimator_params: &id001 !!python/tuple
+      - criterion
+      - max_depth
+      - min_samples_split
+      - min_samples_leaf
+      - min_weight_fraction_leaf
+      - max_features
+      - max_leaf_nodes
+      - min_impurity_decrease
+      - random_state
+      - ccp_alpha
+      - monotonic_cst
+      max_depth: 5
+      max_features: 20
+      max_leaf_nodes: null
+      max_samples: null
+      min_impurity_decrease: 0.0
+      min_samples_leaf: 2
+      min_samples_split: 2
+      min_weight_fraction_leaf: 0.0
+      monotonic_cst: null
+      n_estimators: 100
+      n_jobs: null
+      oob_score: false
+      random_state: null
+      verbose: 0
+      warm_start: false
+    ml_m: !!python/tuple
+    - Random Forest
+    - !!python/object:sklearn.ensemble._forest.RandomForestRegressor
+      _sklearn_version: 1.5.2
+      bootstrap: true
+      ccp_alpha: 0.0
+      class_weight: null
+      criterion: squared_error
+      estimator: !!python/object:sklearn.tree._classes.DecisionTreeRegressor
+        _sklearn_version: 1.5.2
+        ccp_alpha: 0.0
+        class_weight: null
+        criterion: squared_error
+        max_depth: null
+        max_features: null
+        max_leaf_nodes: null
+        min_impurity_decrease: 0.0
+        min_samples_leaf: 1
+        min_samples_split: 2
+        min_weight_fraction_leaf: 0.0
+        monotonic_cst: null
+        random_state: null
+        splitter: best
+      estimator_params: *id001
+      max_depth: 5
+      max_features: 20
+      max_leaf_nodes: null
+      max_samples: null
+      min_impurity_decrease: 0.0
+      min_samples_leaf: 2
+      min_samples_split: 2
+      min_weight_fraction_leaf: 0.0
+      monotonic_cst: null
+      n_estimators: 100
+      n_jobs: null
+      oob_score: false
+      random_state: null
+      verbose: 0
+      warm_start: false
+  - ml_g: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+    ml_m: !!python/tuple
+    - Random Forest
+    - !!python/object:sklearn.ensemble._forest.RandomForestRegressor
+      _sklearn_version: 1.5.2
+      bootstrap: true
+      ccp_alpha: 0.0
+      class_weight: null
+      criterion: squared_error
+      estimator: !!python/object:sklearn.tree._classes.DecisionTreeRegressor
+        _sklearn_version: 1.5.2
+        ccp_alpha: 0.0
+        class_weight: null
+        criterion: squared_error
+        max_depth: null
+        max_features: null
+        max_leaf_nodes: null
+        min_impurity_decrease: 0.0
+        min_samples_leaf: 1
+        min_samples_split: 2
+        min_weight_fraction_leaf: 0.0
+        monotonic_cst: null
+        random_state: null
+        splitter: best
+      estimator_params: *id001
+      max_depth: 5
+      max_features: 20
+      max_leaf_nodes: null
+      max_samples: null
+      min_impurity_decrease: 0.0
+      min_samples_leaf: 2
+      min_samples_split: 2
+      min_weight_fraction_leaf: 0.0
+      monotonic_cst: null
+      n_estimators: 100
+      n_jobs: null
+      oob_score: false
+      random_state: null
+      verbose: 0
+      warm_start: false
+  - ml_g: !!python/tuple
+    - Random Forest
+    - !!python/object:sklearn.ensemble._forest.RandomForestRegressor
+      _sklearn_version: 1.5.2
+      bootstrap: true
+      ccp_alpha: 0.0
+      class_weight: null
+      criterion: squared_error
+      estimator: !!python/object:sklearn.tree._classes.DecisionTreeRegressor
+        _sklearn_version: 1.5.2
+        ccp_alpha: 0.0
+        class_weight: null
+        criterion: squared_error
+        max_depth: null
+        max_features: null
+        max_leaf_nodes: null
+        min_impurity_decrease: 0.0
+        min_samples_leaf: 1
+        min_samples_split: 2
+        min_weight_fraction_leaf: 0.0
+        monotonic_cst: null
+        random_state: null
+        splitter: best
+      estimator_params: *id001
+      max_depth: 5
+      max_features: 20
+      max_leaf_nodes: null
+      max_samples: null
+      min_impurity_decrease: 0.0
+      min_samples_leaf: 2
+      min_samples_split: 2
+      min_weight_fraction_leaf: 0.0
+      monotonic_cst: null
+      n_estimators: 100
+      n_jobs: null
+      oob_score: false
+      random_state: null
+      verbose: 0
+      warm_start: false
+    ml_m: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+  - ml_g: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMRegressor
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.05
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 100
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+    ml_m: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMRegressor
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.05
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 100
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+  score:
+  - partialling out
+  - IV-type
+simulation_parameters:
+  max_runtime: 19800
+  n_jobs: -2
+  random_seed: 42
+  repetitions: 1000
diff --git a/results/plm/plr_ate_coverage.csv b/results/plm/plr_ate_coverage.csv
index e1067cc..e7f02fa 100644
--- a/results/plm/plr_ate_coverage.csv
+++ b/results/plm/plr_ate_coverage.csv
@@ -1,17 +1,21 @@
-Learner g,Learner m,score,level,Coverage,CI Length,Bias,repetition
-Lasso,Lasso,IV-type,0.9,0.881,0.1393979255576113,0.0352891099128789,1000
-Lasso,Lasso,IV-type,0.95,0.945,0.16610287331091153,0.0352891099128789,1000
-Lasso,Lasso,partialling out,0.9,0.908,0.14646362984437974,0.034686755904342816,1000
-Lasso,Lasso,partialling out,0.95,0.956,0.17452217926042807,0.034686755904342816,1000
-Lasso,Random Forest,IV-type,0.9,0.895,0.14672332096879584,0.03621404411832195,1000
-Lasso,Random Forest,IV-type,0.95,0.953,0.17483162032109167,0.03621404411832195,1000
-Lasso,Random Forest,partialling out,0.9,0.816,0.14332193251141256,0.04209132271298422,1000
-Lasso,Random Forest,partialling out,0.95,0.888,0.17077861599008798,0.04209132271298422,1000
-Random Forest,Lasso,IV-type,0.9,0.883,0.14193144590758777,0.03591650651278451,1000
-Random Forest,Lasso,IV-type,0.95,0.951,0.16912174900823193,0.03591650651278451,1000
-Random Forest,Lasso,partialling out,0.9,0.901,0.1519648979095884,0.03615745065828101,1000
-Random Forest,Lasso,partialling out,0.95,0.952,0.181077344474182,0.03615745065828101,1000
-Random Forest,Random Forest,IV-type,0.9,0.893,0.14942288181113228,0.03672076481604481,1000
-Random Forest,Random Forest,IV-type,0.95,0.948,0.17804834546815554,0.03672076481604481,1000
-Random Forest,Random Forest,partialling out,0.9,0.878,0.14635785065361873,0.037417599679567926,1000
-Random Forest,Random Forest,partialling out,0.95,0.946,0.17439613558042621,0.037417599679567926,1000
+Learner g,Learner m,Score,level,Coverage,CI Length,Bias
+LGBM,LGBM,IV-type,0.9,0.881,0.16045495852616187,0.041395241786732526
+LGBM,LGBM,IV-type,0.95,0.929,0.19119387567330545,0.041395241786732526
+LGBM,LGBM,partialling out,0.9,0.843,0.14699796562424566,0.042234936165735856
+LGBM,LGBM,partialling out,0.95,0.91,0.17515887961298732,0.042234936165735856
+Lasso,Lasso,IV-type,0.9,0.88,0.139906736409111,0.03566583231811613
+Lasso,Lasso,IV-type,0.95,0.935,0.16670915883537538,0.03566583231811613
+Lasso,Lasso,partialling out,0.9,0.895,0.14682161377285347,0.03567834426028154
+Lasso,Lasso,partialling out,0.95,0.94,0.1749487434211267,0.03567834426028154
+Lasso,Random Forest,IV-type,0.9,0.886,0.14706644661357227,0.03746782142897475
+Lasso,Random Forest,IV-type,0.95,0.94,0.17524047974475984,0.03746782142897475
+Lasso,Random Forest,partialling out,0.9,0.806,0.14380399961661133,0.04440852985313324
+Lasso,Random Forest,partialling out,0.95,0.869,0.1713530343753106,0.04440852985313324
+Random Forest,Lasso,IV-type,0.9,0.878,0.14220975326271637,0.03662484099631004
+Random Forest,Lasso,IV-type,0.95,0.933,0.16945337267598382,0.03662484099631004
+Random Forest,Lasso,partialling out,0.9,0.893,0.15213074253257877,0.03746402925271374
+Random Forest,Lasso,partialling out,0.95,0.948,0.18127496053117625,0.03746402925271374
+Random Forest,Random Forest,IV-type,0.9,0.886,0.14967937696801667,0.037873686545345016
+Random Forest,Random Forest,IV-type,0.95,0.94,0.17835397829861846,0.037873686545345016
+Random Forest,Random Forest,partialling out,0.9,0.878,0.14693420687636086,0.03830834442193379
+Random Forest,Random Forest,partialling out,0.95,0.934,0.17508290637895235,0.03830834442193379
diff --git a/results/plm/plr_ate_metadata.csv b/results/plm/plr_ate_metadata.csv
new file mode 100644
index 0000000..991fc23
--- /dev/null
+++ b/results/plm/plr_ate_metadata.csv
@@ -0,0 +1,2 @@
+DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
+0.10.dev0,PLRATECoverageSimulation,2025-03-21 15:10,12.47926504611969,3.12.9,scripts/plm/plr_ate_config.yml
diff --git a/scripts/plm/plr_ate_config.yml b/scripts/plm/plr_ate_config.yml
index 479240c..93ee95c 100644
--- a/scripts/plm/plr_ate_config.yml
+++ b/scripts/plm/plr_ate_config.yml
@@ -1,7 +1,7 @@
 # Simulation parameters for PLR ATE Coverage
 
 simulation_parameters:
-  repetitions: 10
+  repetitions: 1000
   max_runtime: 19800 # 5.5 hours in seconds
   random_seed: 42
   n_jobs: -2
@@ -22,6 +22,8 @@ dml_parameters:
       ml_m: ["Random Forest"]
     - ml_g: ["Random Forest"]
       ml_m: ["Lasso"]
+    - ml_g: ["LGBM"]
+      ml_m: ["LGBM"]
 
   score: ["partialling out", "IV-type"]
 

From bf3cf73f2f2942a5c0cf98f5d525df9e77451569 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 15:50:55 +0100
Subject: [PATCH 35/60] add LGBM sim to did

---
 .../src/montecover/did/did_pa_multi.py        |  4 +-
 results/did/did_multi_detailed.csv            | 72 ++++++++++++-------
 results/did/did_multi_eventstudy.csv          | 72 ++++++++++++-------
 results/did/did_multi_group.csv               | 72 ++++++++++++-------
 results/did/did_multi_metadata.csv            |  2 +-
 results/did/did_multi_time.csv                | 72 ++++++++++++-------
 results/did/did_pa_multi_config.yml           | 72 +++++++++++++++++++
 scripts/did/did_pa_multi_config.yml           |  2 +
 8 files changed, 269 insertions(+), 99 deletions(-)

diff --git a/monte-cover/src/montecover/did/did_pa_multi.py b/monte-cover/src/montecover/did/did_pa_multi.py
index b10f6ce..4af123f 100644
--- a/monte-cover/src/montecover/did/did_pa_multi.py
+++ b/monte-cover/src/montecover/did/did_pa_multi.py
@@ -46,7 +46,7 @@ def _process_config_parameters(self):
             if learner["ml_g"][0] == "Linear":
                 learner["ml_g"] = ("Linear", LinearRegression())
             elif learner["ml_g"][0] == "LGBM":
-                learner["ml_g"] = ("LGBM", LGBMRegressor(n_estimators=5, verbose=-1))
+                learner["ml_g"] = ("LGBM", LGBMRegressor(n_estimators=100, learning_rate=0.05, verbose=-1, n_jobs=1))
             else:
                 raise ValueError(f"Unknown learner type: {learner['ml_g']}")
 
@@ -54,7 +54,7 @@ def _process_config_parameters(self):
             if learner["ml_m"][0] == "Linear":
                 learner["ml_m"] = ("Linear", LogisticRegression())
             elif learner["ml_m"][0] == "LGBM":
-                learner["ml_m"] = ("LGBM", LGBMClassifier(n_estimators=5, verbose=-1))
+                learner["ml_m"] = ("LGBM", LGBMClassifier(n_estimators=100, learning_rate=0.05, verbose=-1, n_jobs=1))
             else:
                 raise ValueError(f"Unknown learner type: {learner['ml_m']}")
 
diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
index b985bdb..93e2423 100644
--- a/results/did/did_multi_detailed.csv
+++ b/results/did/did_multi_detailed.csv
@@ -1,25 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.8770833333333333,0.5956631078920439,0.15393126826790132,0.846,0.9280642120939564,1000
-Linear,Linear,experimental,False,1,0.95,0.9353333333333333,0.70977637113606,0.15393126826790132,0.907,1.01868658603788,1000
-Linear,Linear,experimental,False,4,0.9,0.6671666666666666,1.9827195149925345,0.8550657449695865,0.505,2.8772921242320737,1000
-Linear,Linear,experimental,False,4,0.95,0.758,2.362556021493784,0.8550657449695865,0.628,3.2083215653145722,1000
-Linear,Linear,experimental,False,6,0.9,0.9025,2.00173937868972,0.49027975353015657,0.903,2.8952108652661397,1000
-Linear,Linear,experimental,False,6,0.95,0.9499166666666666,2.385219586948198,0.49027975353015657,0.939,3.230482678959989,1000
-Linear,Linear,experimental,True,1,0.9,0.8771666666666667,0.5956064011739037,0.15426338756081115,0.842,0.9278381417397478,1000
-Linear,Linear,experimental,True,1,0.95,0.9339166666666666,0.7097088009137527,0.15426338756081115,0.912,1.0187258089279716,1000
-Linear,Linear,experimental,True,4,0.9,0.6675,1.9828114892881032,0.8559251553294747,0.507,2.8761724678009353,1000
-Linear,Linear,experimental,True,4,0.95,0.7571666666666667,2.362665615626578,0.8559251553294747,0.618,3.204786768191425,1000
-Linear,Linear,experimental,True,6,0.9,0.9008333333333334,2.002017704071254,0.4900985674785569,0.904,2.897432562708396,1000
-Linear,Linear,experimental,True,6,0.95,0.9494166666666666,2.3855512320957373,0.4900985674785569,0.945,3.231599891589011,1000
-Linear,Linear,observational,False,1,0.9,0.9048333333333334,0.6855202939670088,0.16429464571110677,0.905,1.0652155650078814,1000
-Linear,Linear,observational,False,1,0.95,0.9518333333333334,0.8168478123715063,0.16429464571110677,0.951,1.1708094409885172,1000
-Linear,Linear,observational,False,4,0.9,0.7851666666666667,2.7948549075952385,0.8941603312052668,0.689,4.003170404405238,1000
-Linear,Linear,observational,False,4,0.95,0.8580833333333334,3.3302750294286816,0.8941603312052668,0.786,4.4764083966934205,1000
-Linear,Linear,observational,False,6,0.9,0.8918333333333334,2.540792582093424,0.6659145487088738,0.893,3.64735417288717,1000
-Linear,Linear,observational,False,6,0.95,0.9475,3.0275410963583322,0.6659145487088738,0.941,4.076810932574564,1000
-Linear,Linear,observational,True,1,0.9,0.8975,0.6634200132755311,0.16175894761877385,0.888,1.0319404210431198,1000
-Linear,Linear,observational,True,1,0.95,0.94725,0.790513703674064,0.16175894761877385,0.94,1.1332864016074393,1000
-Linear,Linear,observational,True,4,0.9,0.7758333333333334,2.7290002317619733,0.8973641897321528,0.674,3.912420538282281,1000
-Linear,Linear,observational,True,4,0.95,0.8505833333333334,3.2518043432035615,0.8973641897321528,0.792,4.369245108445732,1000
-Linear,Linear,observational,True,6,0.9,0.8846666666666666,2.4411244723625476,0.6398322462292214,0.892,3.513044851639997,1000
-Linear,Linear,observational,True,6,0.95,0.9423333333333334,2.9087792185359564,0.6398322462292214,0.945,3.923376128802193,1000
+LGBM,LGBM,experimental,False,1,0.9,0.5624166666666667,2.2105706879826315,1.0918363543984133,0.392,3.1595897256714163,1000
+LGBM,LGBM,experimental,False,1,0.95,0.6753333333333333,2.634057439965576,1.0918363543984133,0.527,3.5384433304110696,1000
+LGBM,LGBM,experimental,False,4,0.9,0.56225,1.9056063148380724,0.9987888659120285,0.32,2.8139576308162675,1000
+LGBM,LGBM,experimental,False,4,0.95,0.66375,2.270669976098066,0.9987888659120285,0.434,3.1260300175350486,1000
+LGBM,LGBM,experimental,False,6,0.9,0.901,1.9297833165451947,0.4650292370505929,0.894,2.843058037413404,1000
+LGBM,LGBM,experimental,False,6,0.95,0.9490833333333334,2.2994786505136418,0.4650292370505929,0.95,3.1607650795805657,1000
+LGBM,LGBM,experimental,True,1,0.9,0.56125,2.2109595169063447,1.0901779302400194,0.385,3.161199516791711,1000
+LGBM,LGBM,experimental,True,1,0.95,0.6731666666666666,2.6345207582050456,1.0901779302400194,0.52,3.5381076690106985,1000
+LGBM,LGBM,experimental,True,4,0.9,0.5615,1.9055179156566338,0.9982970546992314,0.319,2.8114626499091226,1000
+LGBM,LGBM,experimental,True,4,0.95,0.661,2.2705646419765104,0.9982970546992314,0.438,3.1240482644060696,1000
+LGBM,LGBM,experimental,True,6,0.9,0.90175,1.9300617281970756,0.465536861894574,0.907,2.8427052038630234,1000
+LGBM,LGBM,experimental,True,6,0.95,0.9504166666666666,2.2998103984586393,0.465536861894574,0.954,3.161162346569528,1000
+LGBM,LGBM,observational,False,1,0.9,0.96075,3.860122273027192,0.7973124864002962,0.983,5.809738980637339,1000
+LGBM,LGBM,observational,False,1,0.95,0.9861666666666666,4.599619386848575,0.7973124864002962,0.995,6.430398563283054,1000
+LGBM,LGBM,observational,False,4,0.9,0.961,4.512624613227589,0.9164722703680156,0.971,6.6898615578032725,1000
+LGBM,LGBM,observational,False,4,0.95,0.9855,5.377123880662486,0.9164722703680156,0.98,7.432765016015873,1000
+LGBM,LGBM,observational,False,6,0.9,0.958,3.50270068952298,0.7100629357142844,0.984,5.273421616280179,1000
+LGBM,LGBM,observational,False,6,0.95,0.986,4.17372530151049,0.7100629357142844,0.997,5.841221698395343,1000
+LGBM,LGBM,observational,True,1,0.9,0.9343333333333333,2.712868127930892,0.5826992260811286,0.945,4.106276591884389,1000
+LGBM,LGBM,observational,True,1,0.95,0.9720833333333334,3.232581755864662,0.5826992260811286,0.977,4.540965421627881,1000
+LGBM,LGBM,observational,True,4,0.9,0.92525,3.156071825360632,0.6849012493398391,0.922,4.7143904406177395,1000
+LGBM,LGBM,observational,True,4,0.95,0.9654166666666666,3.7606915344759266,0.6849012493398391,0.961,5.2265508394561335,1000
+LGBM,LGBM,observational,True,6,0.9,0.94475,2.579379320454953,0.5423990875809755,0.961,3.9010126368155924,1000
+LGBM,LGBM,observational,True,6,0.95,0.978,3.073520031036937,0.5423990875809755,0.976,4.315622661457858,1000
+Linear,Linear,experimental,False,1,0.9,0.87675,0.5962556758101943,0.15540620935309896,0.835,0.9294555811504173,1000
+Linear,Linear,experimental,False,1,0.95,0.9341666666666666,0.7104824593611389,0.15540620935309896,0.903,1.0203476694408922,1000
+Linear,Linear,experimental,False,4,0.9,0.6788333333333334,1.9823719036462466,0.8343842929598351,0.543,2.876526568337002,1000
+Linear,Linear,experimental,False,4,0.95,0.7713333333333334,2.36214181702709,0.8343842929598351,0.647,3.2062768555810672,1000
+Linear,Linear,experimental,False,6,0.9,0.8975833333333334,2.001582926468975,0.4883176375232907,0.889,2.8947205466241517,1000
+Linear,Linear,experimental,False,6,0.95,0.951,2.385033162628771,0.4883176375232907,0.936,3.229856365862383,1000
+Linear,Linear,experimental,True,1,0.9,0.8783333333333334,0.5961879895205551,0.15516071674467652,0.835,0.9289596340870964,1000
+Linear,Linear,experimental,True,1,0.95,0.9336666666666666,0.7104018061724501,0.15516071674467652,0.897,1.0199615861026785,1000
+Linear,Linear,experimental,True,4,0.9,0.679,1.982325730340813,0.8350623844266716,0.542,2.874691539814855,1000
+Linear,Linear,experimental,True,4,0.95,0.7708333333333334,2.3620867981401727,0.8350623844266716,0.648,3.2065829645941943,1000
+Linear,Linear,experimental,True,6,0.9,0.8986666666666666,2.0014329000292035,0.4878977948334089,0.898,2.8958152919701714,1000
+Linear,Linear,experimental,True,6,0.95,0.9515833333333333,2.384854395099635,0.4878977948334089,0.944,3.230868272500581,1000
+Linear,Linear,observational,False,1,0.9,0.90175,0.686534548613581,0.16649618454495813,0.902,1.066351549510838,1000
+Linear,Linear,observational,False,1,0.95,0.9520833333333334,0.8180563713252403,0.16649618454495813,0.946,1.171229891264187,1000
+Linear,Linear,observational,False,4,0.9,0.79475,2.8873024002222816,0.9025556348916655,0.704,4.118610356886369,1000
+Linear,Linear,observational,False,4,0.95,0.867,3.4404330112947377,0.9025556348916655,0.806,4.6067910217812065,1000
+Linear,Linear,observational,False,6,0.9,0.90025,2.598132417226769,0.6748383969329592,0.907,3.730754168279962,1000
+Linear,Linear,observational,False,6,0.95,0.9518333333333334,3.0958657240938163,0.6748383969329592,0.95,4.165912075371038,1000
+Linear,Linear,observational,True,1,0.9,0.8975,0.6663493436025194,0.16422406525625022,0.884,1.0360859078980413,1000
+Linear,Linear,observational,True,1,0.95,0.9454166666666667,0.7940042160489305,0.16422406525625022,0.934,1.1384905364687135,1000
+Linear,Linear,observational,True,4,0.9,0.7818333333333334,2.749847403764977,0.8818152982040376,0.703,3.9307697380515574,1000
+Linear,Linear,observational,True,4,0.95,0.8599166666666667,3.2766452808018376,0.8818152982040376,0.797,4.397407804688746,1000
+Linear,Linear,observational,True,6,0.9,0.8920833333333333,2.4622968110379206,0.6399157390802319,0.887,3.543748460254503,1000
+Linear,Linear,observational,True,6,0.95,0.9465833333333333,2.9340076161223876,0.6399157390802319,0.944,3.9562585292222243,1000
diff --git a/results/did/did_multi_eventstudy.csv b/results/did/did_multi_eventstudy.csv
index 909cde3..def27b9 100644
--- a/results/did/did_multi_eventstudy.csv
+++ b/results/did/did_multi_eventstudy.csv
@@ -1,25 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.8676666666666666,0.42543423226976995,0.11378217794214893,0.82,0.607026998501089,1000
-Linear,Linear,experimental,False,1,0.95,0.921,0.5069361549116113,0.11378217794214893,0.893,0.6744173882852083,1000
-Linear,Linear,experimental,False,4,0.9,0.6021666666666666,1.9754395142835157,0.9518163003680035,0.488,2.5538078783006415,1000
-Linear,Linear,experimental,False,4,0.95,0.7053333333333334,2.353881365607505,0.9518163003680035,0.602,2.9021513463156756,1000
-Linear,Linear,experimental,False,6,0.9,0.8995,2.001327059411559,0.49627387221768626,0.899,2.5768571826106514,1000
-Linear,Linear,experimental,False,6,0.95,0.949,2.3847282782249866,0.49627387221768626,0.942,2.9364211171912067,1000
-Linear,Linear,experimental,True,1,0.9,0.8666666666666666,0.42531823168334737,0.1141016026224358,0.814,0.6067463378989032,1000
-Linear,Linear,experimental,True,1,0.95,0.9231666666666666,0.5067979316874602,0.1141016026224358,0.889,0.6733443239100646,1000
-Linear,Linear,experimental,True,4,0.9,0.5973333333333334,1.975365775525183,0.9534736795550611,0.491,2.5532803279562994,1000
-Linear,Linear,experimental,True,4,0.95,0.7055,2.353793500457543,0.9534736795550611,0.61,2.905573717129059,1000
-Linear,Linear,experimental,True,6,0.9,0.8978333333333334,2.0019957931284336,0.4959848232257912,0.9,2.579876093556324,1000
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diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
index ab64a5f..8e6fd2d 100644
--- a/results/did/did_multi_group.csv
+++ b/results/did/did_multi_group.csv
@@ -1,25 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
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+Linear,Linear,observational,True,1,0.95,0.946,0.7039115072233244,0.14453692468281937,0.946,0.8568435231487644,1000
+Linear,Linear,observational,True,4,0.9,0.7816666666666666,3.0525973420953805,0.9863117736959517,0.732,3.805846942979805,1000
+Linear,Linear,observational,True,4,0.95,0.865,3.6373940828390534,0.9863117736959517,0.815,4.339527793994656,1000
+Linear,Linear,observational,True,6,0.9,0.89,2.7202274205001,0.7062614871662574,0.892,3.3901526544863465,1000
+Linear,Linear,observational,True,6,0.95,0.9456666666666667,3.2413508938298894,0.7062614871662574,0.942,3.863150014029504,1000
diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
index 4124b94..ffc86cf 100644
--- a/results/did/did_multi_metadata.csv
+++ b/results/did/did_multi_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.10.dev0,DIDMultiCoverageSimulation,2025-03-21 13:46,3.4916115164756776,3.12.9,scripts/did/did_pa_multi_config.yml
+0.10.dev0,DIDMultiCoverageSimulation,2025-03-21 15:44,15.20009624560674,3.12.9,scripts/did/did_pa_multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
index 06c3c22..da12006 100644
--- a/results/did/did_multi_time.csv
+++ b/results/did/did_multi_time.csv
@@ -1,25 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-Linear,Linear,experimental,False,1,0.9,0.8453333333333334,0.49324862390769003,0.1376326316621036,0.822,0.6316419735152038,1000
-Linear,Linear,experimental,False,1,0.95,0.912,0.5877419865467073,0.1376326316621036,0.891,0.7143067511536322,1000
-Linear,Linear,experimental,False,4,0.9,0.4776666666666667,1.9593068261105524,1.0466452508829949,0.45,2.2507550996198735,1000
-Linear,Linear,experimental,False,4,0.95,0.579,2.3346580819823073,1.0466452508829949,0.556,2.6163918210317183,1000
-Linear,Linear,experimental,False,6,0.9,0.8956666666666666,1.9573896704373563,0.48930237462467674,0.896,2.2565806380862594,1000
-Linear,Linear,experimental,False,6,0.95,0.9463333333333334,2.3323736500969083,0.48930237462467674,0.941,2.62032018987877,1000
-Linear,Linear,experimental,True,1,0.9,0.8463333333333334,0.4931694609078208,0.1378285637524634,0.824,0.6319264464558539,1000
-Linear,Linear,experimental,True,1,0.95,0.9093333333333333,0.587647658014302,0.1378285637524634,0.895,0.7142050985707156,1000
-Linear,Linear,experimental,True,4,0.9,0.473,1.9591517453060092,1.0480526529722811,0.45,2.250777097405993,1000
-Linear,Linear,experimental,True,4,0.95,0.5806666666666667,2.334473291806076,1.0480526529722811,0.55,2.6128556265062666,1000
-Linear,Linear,experimental,True,6,0.9,0.8966666666666666,1.9576188610509668,0.49183824648546365,0.893,2.253956782621074,1000
-Linear,Linear,experimental,True,6,0.95,0.945,2.3326467475573214,0.49183824648546365,0.943,2.6202784765893865,1000
-Linear,Linear,observational,False,1,0.9,0.8953333333333334,0.5968613600047712,0.14788675870016774,0.878,0.7631035273143418,1000
-Linear,Linear,observational,False,1,0.95,0.94,0.7112041765935568,0.14788675870016774,0.945,0.8624624885034761,1000
-Linear,Linear,observational,False,4,0.9,0.6876666666666666,3.0580400071087412,1.0638406360538493,0.653,3.462223480766848,1000
-Linear,Linear,observational,False,4,0.95,0.7736666666666666,3.643879418209516,1.0638406360538493,0.75,4.0319351473266085,1000
-Linear,Linear,observational,False,6,0.9,0.8823333333333334,2.548375931783461,0.6811130775533906,0.871,2.9334540834011715,1000
-Linear,Linear,observational,False,6,0.95,0.9406666666666667,3.036577214850038,0.6811130775533906,0.945,3.4079635812732043,1000
-Linear,Linear,observational,True,1,0.9,0.8806666666666666,0.5725784741025863,0.14380420604106553,0.867,0.7329506080582452,1000
-Linear,Linear,observational,True,1,0.95,0.9413333333333334,0.682269333377637,0.14380420604106553,0.926,0.8286474263809458,1000
-Linear,Linear,observational,True,4,0.9,0.666,2.97181956637082,1.075066363572111,0.635,3.3631545325412926,1000
-Linear,Linear,observational,True,4,0.95,0.7603333333333334,3.5411414263246734,1.075066363572111,0.748,3.9204031440435947,1000
-Linear,Linear,observational,True,6,0.9,0.8823333333333334,2.4376548170067376,0.6493921794587046,0.877,2.808933035095803,1000
-Linear,Linear,observational,True,6,0.95,0.939,2.904644869178221,0.6493921794587046,0.937,3.2552833144907303,1000
+LGBM,LGBM,experimental,False,1,0.9,0.3656666666666667,2.2569933486790243,1.3346952479951693,0.335,2.587283679771306,1000
+LGBM,LGBM,experimental,False,1,0.95,0.5013333333333333,2.689373452004948,1.3346952479951693,0.459,3.0091292065928448,1000
+LGBM,LGBM,experimental,False,4,0.9,0.2906666666666667,1.8450847724208734,1.2779372023374136,0.267,2.1555721241445758,1000
+LGBM,LGBM,experimental,False,4,0.95,0.3963333333333333,2.1985541102952406,1.2779372023374136,0.347,2.4962463971758933,1000
+LGBM,LGBM,experimental,False,6,0.9,0.9003333333333333,1.8659463660137525,0.44755290753308163,0.905,2.18116239110108,1000
+LGBM,LGBM,experimental,False,6,0.95,0.9526666666666667,2.223412232277764,0.44755290753308163,0.949,2.525753365702839,1000
+LGBM,LGBM,experimental,True,1,0.9,0.364,2.2580265033201017,1.334905066108623,0.328,2.586910615526032,1000
+LGBM,LGBM,experimental,True,1,0.95,0.5003333333333333,2.6906045316911844,1.334905066108623,0.455,3.0121528226289516,1000
+LGBM,LGBM,experimental,True,4,0.9,0.294,1.844480535791368,1.2777797661334978,0.253,2.153428316199533,1000
+LGBM,LGBM,experimental,True,4,0.95,0.3903333333333333,2.197834117942994,1.2777797661334978,0.358,2.4936372635204433,1000
+LGBM,LGBM,experimental,True,6,0.9,0.9013333333333333,1.8663144241390512,0.45412326322109603,0.906,2.1805319869022752,1000
+LGBM,LGBM,experimental,True,6,0.95,0.9486666666666667,2.2238508005843793,0.45412326322109603,0.949,2.526280271128774,1000
+LGBM,LGBM,observational,False,1,0.9,0.974,4.015642893221702,0.7766827009394942,0.983,4.816483346098624,1000
+LGBM,LGBM,observational,False,1,0.95,0.993,4.784933635751991,0.7766827009394942,0.997,5.548619730624135,1000
+LGBM,LGBM,observational,False,4,0.9,0.9716666666666667,5.00747786228995,0.9544600040325625,0.977,5.906730263635111,1000
+LGBM,LGBM,observational,False,4,0.95,0.9903333333333334,5.966777896012433,0.9544600040325625,0.989,6.8315972789636215,1000
+LGBM,LGBM,observational,False,6,0.9,0.97,3.433488618358242,0.6551128314455618,0.977,4.135385722477192,1000
+LGBM,LGBM,observational,False,6,0.95,0.9913333333333334,4.091254031997149,0.6551128314455618,0.995,4.752533757091599,1000
+LGBM,LGBM,observational,True,1,0.9,0.9443333333333334,2.7004862065139217,0.5516037755717172,0.949,3.242664571356836,1000
+LGBM,LGBM,observational,True,1,0.95,0.9786666666666666,3.217827786490716,0.5516037755717172,0.98,3.733865944469767,1000
+LGBM,LGBM,observational,True,4,0.9,0.9203333333333333,3.371353319490959,0.6969306645265912,0.905,3.989978424283686,1000
+LGBM,LGBM,observational,True,4,0.95,0.9566666666666667,4.017215256781499,0.6969306645265912,0.947,4.600999609348784,1000
+LGBM,LGBM,observational,True,6,0.9,0.9553333333333334,2.470317849174788,0.5011599149291719,0.956,2.9718674567635484,1000
+LGBM,LGBM,observational,True,6,0.95,0.9823333333333334,2.9435652725662735,0.5011599149291719,0.983,3.4173840462150893,1000
+Linear,Linear,experimental,False,1,0.9,0.8493333333333334,0.4935289794342831,0.1370987141180071,0.828,0.6325256653607308,1000
+Linear,Linear,experimental,False,1,0.95,0.9146666666666666,0.5880760507612888,0.1370987141180071,0.89,0.7144061775480237,1000
+Linear,Linear,experimental,False,4,0.9,0.5073333333333333,1.9618911321974573,1.012377950869642,0.485,2.2523669014616714,1000
+Linear,Linear,experimental,False,4,0.95,0.6206666666666666,2.33773747261766,1.012377950869642,0.592,2.6173574991154327,1000
+Linear,Linear,experimental,False,6,0.9,0.8886666666666666,1.9559181106622394,0.48244705653555303,0.89,2.254488348027745,1000
+Linear,Linear,experimental,False,6,0.95,0.948,2.33062017847301,0.48244705653555303,0.952,2.6170327863809786,1000
+Linear,Linear,experimental,True,1,0.9,0.852,0.493460294448869,0.13685608241876163,0.827,0.6320487920692786,1000
+Linear,Linear,experimental,True,1,0.95,0.915,0.5879942075531853,0.13685608241876163,0.894,0.7142086115473791,1000
+Linear,Linear,experimental,True,4,0.9,0.5073333333333333,1.9617118526474717,1.0141621639498686,0.479,2.250962858505643,1000
+Linear,Linear,experimental,True,4,0.95,0.6203333333333334,2.337523847857755,1.0141621639498686,0.594,2.618856401518769,1000
+Linear,Linear,experimental,True,6,0.9,0.8953333333333334,1.9559523702787176,0.48080345349863246,0.893,2.2527473173908787,1000
+Linear,Linear,experimental,True,6,0.95,0.9473333333333334,2.3306610013239437,0.48080345349863246,0.955,2.618192170363342,1000
+Linear,Linear,observational,False,1,0.9,0.8983333333333333,0.5964425724191427,0.14623943628258027,0.887,0.7624888261759377,1000
+Linear,Linear,observational,False,1,0.95,0.9503333333333334,0.7107051604066116,0.14623943628258027,0.939,0.8622181838697768,1000
+Linear,Linear,observational,False,4,0.9,0.6906666666666667,3.197042848562427,1.0884035855850052,0.652,3.6067317562485295,1000
+Linear,Linear,observational,False,4,0.95,0.788,3.8095115197740124,1.0884035855850052,0.767,4.20942088992997,1000
+Linear,Linear,observational,False,6,0.9,0.8963333333333334,2.638912550074715,0.689163224539215,0.895,3.0353798469910536,1000
+Linear,Linear,observational,False,6,0.95,0.9473333333333334,3.144458249505861,0.689163224539215,0.948,3.5284906386118644,1000
+Linear,Linear,observational,True,1,0.9,0.887,0.5726290497982203,0.14337121919425516,0.874,0.7319181746308432,1000
+Linear,Linear,observational,True,1,0.95,0.9386666666666666,0.6823295980360305,0.14337121919425516,0.93,0.8280502502130269,1000
+Linear,Linear,observational,True,4,0.9,0.677,2.99370554568291,1.0627549900400246,0.642,3.383972541727221,1000
+Linear,Linear,observational,True,4,0.95,0.772,3.5672201791785585,1.0627549900400246,0.741,3.942030692640843,1000
+Linear,Linear,observational,True,6,0.9,0.8893333333333334,2.4790632900480443,0.6526320367848093,0.875,2.854598200173848,1000
+Linear,Linear,observational,True,6,0.95,0.9393333333333334,2.9539861081103305,0.6526320367848093,0.941,3.314585142761016,1000
diff --git a/results/did/did_pa_multi_config.yml b/results/did/did_pa_multi_config.yml
index f7579ad..944e713 100644
--- a/results/did/did_pa_multi_config.yml
+++ b/results/did/did_pa_multi_config.yml
@@ -41,6 +41,78 @@ dml_parameters:
       tol: 0.0001
       verbose: 0
       warm_start: false
+  - ml_g: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMRegressor
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.05
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 100
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+    ml_m: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMClassifier
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.05
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 100
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
   score:
   - observational
   - experimental
diff --git a/scripts/did/did_pa_multi_config.yml b/scripts/did/did_pa_multi_config.yml
index 396085c..04f11a8 100644
--- a/scripts/did/did_pa_multi_config.yml
+++ b/scripts/did/did_pa_multi_config.yml
@@ -15,6 +15,8 @@ dml_parameters:
   learners:
     - ml_g: ["Linear"]
       ml_m: ["Linear"]
+    - ml_g: ["LGBM"]
+      ml_m: ["LGBM"]
 
   score:
     - observational # Standard DML score

From ab79662ba1ba3c77eb06ce77894339dca2ae56ad Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 15:55:37 +0100
Subject: [PATCH 36/60] add count to plr repetition

---
 monte-cover/src/montecover/plm/plr_ate.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/monte-cover/src/montecover/plm/plr_ate.py b/monte-cover/src/montecover/plm/plr_ate.py
index e6119c6..a58b471 100644
--- a/monte-cover/src/montecover/plm/plr_ate.py
+++ b/monte-cover/src/montecover/plm/plr_ate.py
@@ -118,6 +118,7 @@ def summarize_results(self):
             "Coverage": "mean",
             "CI Length": "mean",
             "Bias": "mean",
+            "repetition": "count",
         }
 
         # Aggregate results (possibly multiple result dfs)

From 50996f8058319f5071770d23eaf1eb95f79a157b Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 17:44:41 +0100
Subject: [PATCH 37/60] new cov res

---
 results/plm/plr_ate_coverage.csv | 42 ++++++++++++++++----------------
 results/plm/plr_ate_metadata.csv |  2 +-
 2 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/results/plm/plr_ate_coverage.csv b/results/plm/plr_ate_coverage.csv
index e7f02fa..f801f58 100644
--- a/results/plm/plr_ate_coverage.csv
+++ b/results/plm/plr_ate_coverage.csv
@@ -1,21 +1,21 @@
-Learner g,Learner m,Score,level,Coverage,CI Length,Bias
-LGBM,LGBM,IV-type,0.9,0.881,0.16045495852616187,0.041395241786732526
-LGBM,LGBM,IV-type,0.95,0.929,0.19119387567330545,0.041395241786732526
-LGBM,LGBM,partialling out,0.9,0.843,0.14699796562424566,0.042234936165735856
-LGBM,LGBM,partialling out,0.95,0.91,0.17515887961298732,0.042234936165735856
-Lasso,Lasso,IV-type,0.9,0.88,0.139906736409111,0.03566583231811613
-Lasso,Lasso,IV-type,0.95,0.935,0.16670915883537538,0.03566583231811613
-Lasso,Lasso,partialling out,0.9,0.895,0.14682161377285347,0.03567834426028154
-Lasso,Lasso,partialling out,0.95,0.94,0.1749487434211267,0.03567834426028154
-Lasso,Random Forest,IV-type,0.9,0.886,0.14706644661357227,0.03746782142897475
-Lasso,Random Forest,IV-type,0.95,0.94,0.17524047974475984,0.03746782142897475
-Lasso,Random Forest,partialling out,0.9,0.806,0.14380399961661133,0.04440852985313324
-Lasso,Random Forest,partialling out,0.95,0.869,0.1713530343753106,0.04440852985313324
-Random Forest,Lasso,IV-type,0.9,0.878,0.14220975326271637,0.03662484099631004
-Random Forest,Lasso,IV-type,0.95,0.933,0.16945337267598382,0.03662484099631004
-Random Forest,Lasso,partialling out,0.9,0.893,0.15213074253257877,0.03746402925271374
-Random Forest,Lasso,partialling out,0.95,0.948,0.18127496053117625,0.03746402925271374
-Random Forest,Random Forest,IV-type,0.9,0.886,0.14967937696801667,0.037873686545345016
-Random Forest,Random Forest,IV-type,0.95,0.94,0.17835397829861846,0.037873686545345016
-Random Forest,Random Forest,partialling out,0.9,0.878,0.14693420687636086,0.03830834442193379
-Random Forest,Random Forest,partialling out,0.95,0.934,0.17508290637895235,0.03830834442193379
+Learner g,Learner m,Score,level,Coverage,CI Length,Bias,repetition
+LGBM,LGBM,IV-type,0.9,0.882,0.16071165852972294,0.04006345992726611,1000
+LGBM,LGBM,IV-type,0.95,0.938,0.19149975259363877,0.04006345992726611,1000
+LGBM,LGBM,partialling out,0.9,0.842,0.14721491277053025,0.042110152606416054,1000
+LGBM,LGBM,partialling out,0.95,0.906,0.17541738808225127,0.042110152606416054,1000
+Lasso,Lasso,IV-type,0.9,0.866,0.14050109787041962,0.03660923399902996,1000
+Lasso,Lasso,IV-type,0.95,0.915,0.16741738419894311,0.03660923399902996,1000
+Lasso,Lasso,partialling out,0.9,0.879,0.1474715771739181,0.03630990507775283,1000
+Lasso,Lasso,partialling out,0.95,0.931,0.17572322258236175,0.03630990507775283,1000
+Lasso,Random Forest,IV-type,0.9,0.886,0.1474535680840687,0.03770184842456424,1000
+Lasso,Random Forest,IV-type,0.95,0.939,0.17570176342823354,0.03770184842456424,1000
+Lasso,Random Forest,partialling out,0.9,0.785,0.14384571087180478,0.045773052530728796,1000
+Lasso,Random Forest,partialling out,0.95,0.883,0.17140273640142992,0.045773052530728796,1000
+Random Forest,Lasso,IV-type,0.9,0.879,0.14285466535066815,0.036599158846521436,1000
+Random Forest,Lasso,IV-type,0.95,0.93,0.17022183282640022,0.036599158846521436,1000
+Random Forest,Lasso,partialling out,0.9,0.892,0.15286076950172306,0.03825604774970179,1000
+Random Forest,Lasso,partialling out,0.95,0.939,0.1821448413180263,0.03825604774970179,1000
+Random Forest,Random Forest,IV-type,0.9,0.885,0.15036250130159673,0.037935837399518176,1000
+Random Forest,Random Forest,IV-type,0.95,0.944,0.17916797114809854,0.037935837399518176,1000
+Random Forest,Random Forest,partialling out,0.9,0.881,0.14718055225415835,0.03854156805613427,1000
+Random Forest,Random Forest,partialling out,0.95,0.934,0.17537644500167823,0.03854156805613427,1000
diff --git a/results/plm/plr_ate_metadata.csv b/results/plm/plr_ate_metadata.csv
index 991fc23..b96ed32 100644
--- a/results/plm/plr_ate_metadata.csv
+++ b/results/plm/plr_ate_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.10.dev0,PLRATECoverageSimulation,2025-03-21 15:10,12.47926504611969,3.12.9,scripts/plm/plr_ate_config.yml
+0.10.dev0,PLRATECoverageSimulation,2025-03-21 17:43,12.395700534184774,3.12.9,scripts/plm/plr_ate_config.yml

From 2ca4de97195db05625aba30edb8715954b363108 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Fri, 21 Mar 2025 17:50:45 +0100
Subject: [PATCH 38/60] update plr qmd

---
 .gitignore      |  3 +--
 doc/plm/plr.qmd | 10 +++++-----
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/.gitignore b/.gitignore
index 67d3ba2..628f882 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,5 +2,4 @@ __pycache__/
 
 # Logs
 monte-cover/logs/
-*.log
-run_*.out
\ No newline at end of file
+*.log
\ No newline at end of file
diff --git a/doc/plm/plr.qmd b/doc/plm/plr.qmd
index 61d482c..0a0dbcc 100644
--- a/doc/plm/plr.qmd
+++ b/doc/plm/plr.qmd
@@ -90,11 +90,11 @@ display_columns = ["Learner g", "Learner m", "Bias", "CI Length", "Coverage"]
 ### Partialling out
 
 ```{python}
-#| echo: false
+# | echo: false
 score = "partialling out"
 level = 0.95
 
-df_ate_95 = df[(df['level'] == level) & (df["score"] == score)][display_columns]
+df_ate_95 = df[(df["level"] == level) & (df["Score"] == score)][display_columns]
 df_ate_95.rename(columns={"Learner g": "Learner l"}, inplace=True)
 make_pretty(df_ate_95, level, n_rep)
 ```
@@ -104,7 +104,7 @@ make_pretty(df_ate_95, level, n_rep)
 score = "partialling out"
 level = 0.9
 
-df_ate_9 = df[(df['level'] == level) & (df["score"] == score)][display_columns]
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
 df_ate_9.rename(columns={"Learner g": "Learner l"}, inplace=True)
 make_pretty(df_ate_9, level, n_rep)
 ```
@@ -118,7 +118,7 @@ For the IV-type score, the learners `ml_l` and `ml_g` are both set to the same t
 score = "IV-type"
 level = 0.95
 
-df_ate_95 = df[(df['level'] == level) & (df["score"] == score)][display_columns]
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
 make_pretty(df_ate_95, level, n_rep)
 ```
 
@@ -127,7 +127,7 @@ make_pretty(df_ate_95, level, n_rep)
 score = "IV-type"
 level = 0.9
 
-df_ate_9 = df[(df['level'] == level) & (df["score"] == score)][display_columns]
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
 make_pretty(df_ate_9, level, n_rep)
 ```
 

From 42551e4e12d29d0c25a40c3bb214de82ea5abe50 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Sat, 22 Mar 2025 13:16:53 +0100
Subject: [PATCH 39/60] update did sim lgbm

---
 .../src/montecover/did/did_pa_multi.py        |  4 +-
 results/did/did_multi_detailed.csv            | 96 +++++++++----------
 results/did/did_multi_eventstudy.csv          | 96 +++++++++----------
 results/did/did_multi_group.csv               | 96 +++++++++----------
 results/did/did_multi_metadata.csv            |  2 +-
 results/did/did_multi_time.csv                | 96 +++++++++----------
 results/did/did_pa_multi_config.yml           | 10 +-
 scripts/did/did_pa_multi_config.yml           |  2 +-
 8 files changed, 201 insertions(+), 201 deletions(-)

diff --git a/monte-cover/src/montecover/did/did_pa_multi.py b/monte-cover/src/montecover/did/did_pa_multi.py
index 4af123f..2c4e601 100644
--- a/monte-cover/src/montecover/did/did_pa_multi.py
+++ b/monte-cover/src/montecover/did/did_pa_multi.py
@@ -46,7 +46,7 @@ def _process_config_parameters(self):
             if learner["ml_g"][0] == "Linear":
                 learner["ml_g"] = ("Linear", LinearRegression())
             elif learner["ml_g"][0] == "LGBM":
-                learner["ml_g"] = ("LGBM", LGBMRegressor(n_estimators=100, learning_rate=0.05, verbose=-1, n_jobs=1))
+                learner["ml_g"] = ("LGBM", LGBMRegressor(n_estimators=500, learning_rate=0.02, verbose=-1, n_jobs=1))
             else:
                 raise ValueError(f"Unknown learner type: {learner['ml_g']}")
 
@@ -54,7 +54,7 @@ def _process_config_parameters(self):
             if learner["ml_m"][0] == "Linear":
                 learner["ml_m"] = ("Linear", LogisticRegression())
             elif learner["ml_m"][0] == "LGBM":
-                learner["ml_m"] = ("LGBM", LGBMClassifier(n_estimators=100, learning_rate=0.05, verbose=-1, n_jobs=1))
+                learner["ml_m"] = ("LGBM", LGBMClassifier(n_estimators=500, learning_rate=0.02, verbose=-1, n_jobs=1))
             else:
                 raise ValueError(f"Unknown learner type: {learner['ml_m']}")
 
diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
index 93e2423..e584ef5 100644
--- a/results/did/did_multi_detailed.csv
+++ b/results/did/did_multi_detailed.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-LGBM,LGBM,experimental,False,1,0.9,0.5624166666666667,2.2105706879826315,1.0918363543984133,0.392,3.1595897256714163,1000
-LGBM,LGBM,experimental,False,1,0.95,0.6753333333333333,2.634057439965576,1.0918363543984133,0.527,3.5384433304110696,1000
-LGBM,LGBM,experimental,False,4,0.9,0.56225,1.9056063148380724,0.9987888659120285,0.32,2.8139576308162675,1000
-LGBM,LGBM,experimental,False,4,0.95,0.66375,2.270669976098066,0.9987888659120285,0.434,3.1260300175350486,1000
-LGBM,LGBM,experimental,False,6,0.9,0.901,1.9297833165451947,0.4650292370505929,0.894,2.843058037413404,1000
-LGBM,LGBM,experimental,False,6,0.95,0.9490833333333334,2.2994786505136418,0.4650292370505929,0.95,3.1607650795805657,1000
-LGBM,LGBM,experimental,True,1,0.9,0.56125,2.2109595169063447,1.0901779302400194,0.385,3.161199516791711,1000
-LGBM,LGBM,experimental,True,1,0.95,0.6731666666666666,2.6345207582050456,1.0901779302400194,0.52,3.5381076690106985,1000
-LGBM,LGBM,experimental,True,4,0.9,0.5615,1.9055179156566338,0.9982970546992314,0.319,2.8114626499091226,1000
-LGBM,LGBM,experimental,True,4,0.95,0.661,2.2705646419765104,0.9982970546992314,0.438,3.1240482644060696,1000
-LGBM,LGBM,experimental,True,6,0.9,0.90175,1.9300617281970756,0.465536861894574,0.907,2.8427052038630234,1000
-LGBM,LGBM,experimental,True,6,0.95,0.9504166666666666,2.2998103984586393,0.465536861894574,0.954,3.161162346569528,1000
-LGBM,LGBM,observational,False,1,0.9,0.96075,3.860122273027192,0.7973124864002962,0.983,5.809738980637339,1000
-LGBM,LGBM,observational,False,1,0.95,0.9861666666666666,4.599619386848575,0.7973124864002962,0.995,6.430398563283054,1000
-LGBM,LGBM,observational,False,4,0.9,0.961,4.512624613227589,0.9164722703680156,0.971,6.6898615578032725,1000
-LGBM,LGBM,observational,False,4,0.95,0.9855,5.377123880662486,0.9164722703680156,0.98,7.432765016015873,1000
-LGBM,LGBM,observational,False,6,0.9,0.958,3.50270068952298,0.7100629357142844,0.984,5.273421616280179,1000
-LGBM,LGBM,observational,False,6,0.95,0.986,4.17372530151049,0.7100629357142844,0.997,5.841221698395343,1000
-LGBM,LGBM,observational,True,1,0.9,0.9343333333333333,2.712868127930892,0.5826992260811286,0.945,4.106276591884389,1000
-LGBM,LGBM,observational,True,1,0.95,0.9720833333333334,3.232581755864662,0.5826992260811286,0.977,4.540965421627881,1000
-LGBM,LGBM,observational,True,4,0.9,0.92525,3.156071825360632,0.6849012493398391,0.922,4.7143904406177395,1000
-LGBM,LGBM,observational,True,4,0.95,0.9654166666666666,3.7606915344759266,0.6849012493398391,0.961,5.2265508394561335,1000
-LGBM,LGBM,observational,True,6,0.9,0.94475,2.579379320454953,0.5423990875809755,0.961,3.9010126368155924,1000
-LGBM,LGBM,observational,True,6,0.95,0.978,3.073520031036937,0.5423990875809755,0.976,4.315622661457858,1000
-Linear,Linear,experimental,False,1,0.9,0.87675,0.5962556758101943,0.15540620935309896,0.835,0.9294555811504173,1000
-Linear,Linear,experimental,False,1,0.95,0.9341666666666666,0.7104824593611389,0.15540620935309896,0.903,1.0203476694408922,1000
-Linear,Linear,experimental,False,4,0.9,0.6788333333333334,1.9823719036462466,0.8343842929598351,0.543,2.876526568337002,1000
-Linear,Linear,experimental,False,4,0.95,0.7713333333333334,2.36214181702709,0.8343842929598351,0.647,3.2062768555810672,1000
-Linear,Linear,experimental,False,6,0.9,0.8975833333333334,2.001582926468975,0.4883176375232907,0.889,2.8947205466241517,1000
-Linear,Linear,experimental,False,6,0.95,0.951,2.385033162628771,0.4883176375232907,0.936,3.229856365862383,1000
-Linear,Linear,experimental,True,1,0.9,0.8783333333333334,0.5961879895205551,0.15516071674467652,0.835,0.9289596340870964,1000
-Linear,Linear,experimental,True,1,0.95,0.9336666666666666,0.7104018061724501,0.15516071674467652,0.897,1.0199615861026785,1000
-Linear,Linear,experimental,True,4,0.9,0.679,1.982325730340813,0.8350623844266716,0.542,2.874691539814855,1000
-Linear,Linear,experimental,True,4,0.95,0.7708333333333334,2.3620867981401727,0.8350623844266716,0.648,3.2065829645941943,1000
-Linear,Linear,experimental,True,6,0.9,0.8986666666666666,2.0014329000292035,0.4878977948334089,0.898,2.8958152919701714,1000
-Linear,Linear,experimental,True,6,0.95,0.9515833333333333,2.384854395099635,0.4878977948334089,0.944,3.230868272500581,1000
-Linear,Linear,observational,False,1,0.9,0.90175,0.686534548613581,0.16649618454495813,0.902,1.066351549510838,1000
-Linear,Linear,observational,False,1,0.95,0.9520833333333334,0.8180563713252403,0.16649618454495813,0.946,1.171229891264187,1000
-Linear,Linear,observational,False,4,0.9,0.79475,2.8873024002222816,0.9025556348916655,0.704,4.118610356886369,1000
-Linear,Linear,observational,False,4,0.95,0.867,3.4404330112947377,0.9025556348916655,0.806,4.6067910217812065,1000
-Linear,Linear,observational,False,6,0.9,0.90025,2.598132417226769,0.6748383969329592,0.907,3.730754168279962,1000
-Linear,Linear,observational,False,6,0.95,0.9518333333333334,3.0958657240938163,0.6748383969329592,0.95,4.165912075371038,1000
-Linear,Linear,observational,True,1,0.9,0.8975,0.6663493436025194,0.16422406525625022,0.884,1.0360859078980413,1000
-Linear,Linear,observational,True,1,0.95,0.9454166666666667,0.7940042160489305,0.16422406525625022,0.934,1.1384905364687135,1000
-Linear,Linear,observational,True,4,0.9,0.7818333333333334,2.749847403764977,0.8818152982040376,0.703,3.9307697380515574,1000
-Linear,Linear,observational,True,4,0.95,0.8599166666666667,3.2766452808018376,0.8818152982040376,0.797,4.397407804688746,1000
-Linear,Linear,observational,True,6,0.9,0.8920833333333333,2.4622968110379206,0.6399157390802319,0.887,3.543748460254503,1000
-Linear,Linear,observational,True,6,0.95,0.9465833333333333,2.9340076161223876,0.6399157390802319,0.944,3.9562585292222243,1000
+LGBM,LGBM,experimental,False,1,0.9,0.3929166666666667,0.6687029507520728,0.4569473540080381,0.066,1.0019963477166332,1000
+LGBM,LGBM,experimental,False,1,0.95,0.48991666666666667,0.7968087119452796,0.4569473540080381,0.131,1.1107772389821786,1000
+LGBM,LGBM,experimental,False,4,0.9,0.5314166666666666,0.5827863000707186,0.32927572636155183,0.206,0.8969333386170385,1000
+LGBM,LGBM,experimental,False,4,0.95,0.6208333333333333,0.6944327082397956,0.32927572636155183,0.298,0.9870085765972824,1000
+LGBM,LGBM,experimental,False,6,0.9,0.8985833333333334,0.5798291764546274,0.1415015522676616,0.902,0.8926496847901934,1000
+LGBM,LGBM,experimental,False,6,0.95,0.9505,0.6909090781183035,0.1415015522676616,0.94,0.9826119522997802,1000
+LGBM,LGBM,experimental,True,1,0.9,0.3985833333333333,0.6685234379502083,0.4558614977424509,0.073,1.0027027615662685,1000
+LGBM,LGBM,experimental,True,1,0.95,0.49141666666666667,0.7965948092486183,0.4558614977424509,0.14,1.1116972038295265,1000
+LGBM,LGBM,experimental,True,4,0.9,0.529,0.5827218897275354,0.3290643441251289,0.21,0.8975401350497161,1000
+LGBM,LGBM,experimental,True,4,0.95,0.6195833333333334,0.6943559585820737,0.3290643441251289,0.286,0.988165661809689,1000
+LGBM,LGBM,experimental,True,6,0.9,0.9005833333333334,0.5798357897778391,0.14062961792513812,0.903,0.8920158735259474,1000
+LGBM,LGBM,experimental,True,6,0.95,0.9526666666666667,0.6909169583789544,0.14062961792513812,0.946,0.9820497847451054,1000
+LGBM,LGBM,observational,False,1,0.9,0.91325,2.7335893497080668,0.7032100492915742,0.97,4.24966855110796,1000
+LGBM,LGBM,observational,False,1,0.95,0.9674166666666666,3.2572726145121886,0.7032100492915742,0.989,4.665238891989982,1000
+LGBM,LGBM,observational,False,4,0.9,0.9081666666666667,3.502596227758532,0.9581715859756315,0.97,5.396052160776311,1000
+LGBM,LGBM,observational,False,4,0.95,0.9659166666666666,4.173600827640763,0.9581715859756315,0.991,5.937512318038689,1000
+LGBM,LGBM,observational,False,6,0.9,0.9283333333333333,2.171679918677507,0.507690184177682,0.966,3.389992202359619,1000
+LGBM,LGBM,observational,False,6,0.95,0.9716666666666667,2.5877162300730143,0.507690184177682,0.986,3.7190046001183736,1000
+LGBM,LGBM,observational,True,1,0.9,0.9136666666666666,1.1116368737676119,0.26925529063955034,0.94,1.7337204258041814,1000
+LGBM,LGBM,observational,True,1,0.95,0.95975,1.3245970345150337,0.26925529063955034,0.979,1.901763207693331,1000
+LGBM,LGBM,observational,True,4,0.9,0.9205,1.415253033640148,0.329332847066125,0.923,2.1884940627932736,1000
+LGBM,LGBM,observational,True,4,0.95,0.964,1.6863780031823952,0.329332847066125,0.964,2.405714926928363,1000
+LGBM,LGBM,observational,True,6,0.9,0.9066666666666666,1.0240923600589003,0.2484225935648514,0.917,1.6017734165577624,1000
+LGBM,LGBM,observational,True,6,0.95,0.9585833333333333,1.220281312373145,0.2484225935648514,0.961,1.7565712317977673,1000
+Linear,Linear,experimental,False,1,0.9,0.8515,0.29472504253429116,0.0813922749648889,0.765,0.45946653721752106,1000
+Linear,Linear,experimental,False,1,0.95,0.9171666666666666,0.35118654890882184,0.0813922749648889,0.861,0.5047523635861321,1000
+Linear,Linear,experimental,False,4,0.9,0.30075,0.975812057155266,0.8193739142204116,0.036,1.4138427377009106,1000
+Linear,Linear,experimental,False,4,0.95,0.3815,1.1627517831169838,0.8193739142204116,0.067,1.57605877958281,1000
+Linear,Linear,experimental,False,6,0.9,0.89825,0.9829577849827824,0.24024745112751242,0.91,1.4202537375432176,1000
+Linear,Linear,experimental,False,6,0.95,0.9493333333333334,1.1712664429966075,0.24024745112751242,0.951,1.5831655301216463,1000
+Linear,Linear,experimental,True,1,0.9,0.8509166666666667,0.2947253078468344,0.08140709028680689,0.762,0.4593988323351278,1000
+Linear,Linear,experimental,True,1,0.95,0.91725,0.3511868650482161,0.08140709028680689,0.864,0.5042913385772815,1000
+Linear,Linear,experimental,True,4,0.9,0.3020833333333333,0.9757488994792876,0.819502522016252,0.039,1.412809617036458,1000
+Linear,Linear,experimental,True,4,0.95,0.3835,1.1626765261043008,0.819502522016252,0.065,1.5757772289647831,1000
+Linear,Linear,experimental,True,6,0.9,0.897,0.9830229866645744,0.24034574478609086,0.908,1.420394273430355,1000
+Linear,Linear,experimental,True,6,0.95,0.9499166666666666,1.171344135592441,0.24034574478609086,0.956,1.5848182567435658,1000
+Linear,Linear,observational,False,1,0.9,0.8973333333333333,0.3186240551118928,0.0778003450354372,0.89,0.49548723415593937,1000
+Linear,Linear,observational,False,1,0.95,0.9491666666666666,0.379663978845845,0.0778003450354372,0.946,0.5446256028987391,1000
+Linear,Linear,observational,False,4,0.9,0.4170833333333333,1.2358847166873828,0.8010746877401096,0.182,1.7662191422551763,1000
+Linear,Linear,observational,False,4,0.95,0.5161666666666667,1.4726474709121475,0.8010746877401096,0.274,1.9752694540622302,1000
+Linear,Linear,observational,False,6,0.9,0.8986666666666666,1.0267086469519957,0.25326747402342975,0.903,1.4829059870672159,1000
+Linear,Linear,observational,False,6,0.95,0.948,1.223398810489494,0.25326747402342975,0.958,1.654277564832196,1000
+Linear,Linear,observational,True,1,0.9,0.8955,0.316626811484083,0.07772309002866071,0.884,0.4926269679922508,1000
+Linear,Linear,observational,True,1,0.95,0.9470833333333334,0.37728411627648417,0.07772309002866071,0.941,0.5413306341435754,1000
+Linear,Linear,observational,True,4,0.9,0.4099166666666667,1.2341893457828894,0.801242331888525,0.186,1.7648003716701655,1000
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diff --git a/results/did/did_multi_eventstudy.csv b/results/did/did_multi_eventstudy.csv
index def27b9..43df8e7 100644
--- a/results/did/did_multi_eventstudy.csv
+++ b/results/did/did_multi_eventstudy.csv
@@ -1,49 +1,49 @@
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diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
index 8e6fd2d..c3ea832 100644
--- a/results/did/did_multi_group.csv
+++ b/results/did/did_multi_group.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
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-Linear,Linear,experimental,True,6,0.95,0.951,2.628838139168606,0.539245881315392,0.947,3.1603971924586123,1000
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diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
index ffc86cf..cf6d79f 100644
--- a/results/did/did_multi_metadata.csv
+++ b/results/did/did_multi_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.10.dev0,DIDMultiCoverageSimulation,2025-03-21 15:44,15.20009624560674,3.12.9,scripts/did/did_pa_multi_config.yml
+0.10.dev0,DIDMultiCoverageSimulation,2025-03-22 13:07,161.55725783109665,3.12.9,scripts/did/did_pa_multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
index da12006..69a4f0a 100644
--- a/results/did/did_multi_time.csv
+++ b/results/did/did_multi_time.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
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+LGBM,LGBM,experimental,True,1,0.95,0.1713333333333333,0.8035855759344999,0.5864355668337375,0.123,0.9238506660612901,1000
+LGBM,LGBM,experimental,True,4,0.9,0.23166666666666666,0.5441212033941374,0.4171381557101547,0.17,0.6607250720722284,1000
+LGBM,LGBM,experimental,True,4,0.95,0.3196666666666667,0.6483604038698857,0.4171381557101547,0.245,0.7579132011887949,1000
+LGBM,LGBM,experimental,True,6,0.9,0.9063333333333333,0.5394904332086611,0.12863612300950375,0.903,0.6565743997497494,1000
+LGBM,LGBM,experimental,True,6,0.95,0.9523333333333334,0.6428425008568152,0.12863612300950375,0.955,0.7532450411959063,1000
+LGBM,LGBM,observational,False,1,0.9,0.8943333333333334,2.905893095599295,0.7648921426349597,0.92,3.588646368039136,1000
+LGBM,LGBM,observational,False,1,0.95,0.9563333333333334,3.462585191154069,0.7648921426349597,0.975,4.0996633280531105,1000
+LGBM,LGBM,observational,False,4,0.9,0.8876666666666666,3.9146026798923885,1.1190311469389873,0.92,4.790575517259599,1000
+LGBM,LGBM,observational,False,4,0.95,0.963,4.664536795649732,1.1190311469389873,0.982,5.486504879391152,1000
+LGBM,LGBM,observational,False,6,0.9,0.942,2.009962087567071,0.4418856843130645,0.958,2.5103783382326825,1000
+LGBM,LGBM,observational,False,6,0.95,0.9816666666666666,2.3950175489011016,0.4418856843130645,0.984,2.8615887060271743,1000
+LGBM,LGBM,observational,True,1,0.9,0.9303333333333333,1.0974116056549843,0.25912148830825843,0.938,1.3607720502797749,1000
+LGBM,LGBM,observational,True,1,0.95,0.969,1.3076465820769951,0.25912148830825843,0.978,1.5521920303181904,1000
+LGBM,LGBM,observational,True,4,0.9,0.9416666666666667,1.472333292909054,0.3205121679339935,0.94,1.8014934435044807,1000
+LGBM,LGBM,observational,True,4,0.95,0.9743333333333334,1.7543933271979495,0.3205121679339935,0.98,2.0658871704252246,1000
+LGBM,LGBM,observational,True,6,0.9,0.915,0.9286745626922529,0.21897181277799815,0.92,1.15681026947425,1000
+LGBM,LGBM,observational,True,6,0.95,0.9626666666666667,1.1065839941081885,0.21897181277799815,0.96,1.3188454688519913,1000
+Linear,Linear,experimental,False,1,0.9,0.7993333333333333,0.24424430193356436,0.07551069080931783,0.741,0.3130913040114941,1000
+Linear,Linear,experimental,False,1,0.95,0.8723333333333334,0.2910350364160617,0.07551069080931783,0.838,0.3538500363992679,1000
+Linear,Linear,experimental,False,4,0.9,0.036333333333333336,0.9660507125397605,1.0827206081770897,0.031,1.107819485412717,1000
+Linear,Linear,experimental,False,4,0.95,0.06233333333333333,1.151120423600494,1.0827206081770897,0.05,1.287625024067252,1000
+Linear,Linear,experimental,False,6,0.9,0.8896666666666666,0.9631043832739674,0.2382471311709886,0.887,1.1078250173884234,1000
+Linear,Linear,experimental,False,6,0.95,0.9476666666666667,1.1476096557407096,0.2382471311709886,0.946,1.2863233234339295,1000
+Linear,Linear,experimental,True,1,0.9,0.8,0.2442583284424631,0.07557199890938014,0.732,0.3129294069731057,1000
+Linear,Linear,experimental,True,1,0.95,0.8703333333333334,0.2910517500322886,0.07557199890938014,0.839,0.35392920047397675,1000
+Linear,Linear,experimental,True,4,0.9,0.036333333333333336,0.9660295319926757,1.0828776638271536,0.031,1.1077283731317225,1000
+Linear,Linear,experimental,True,4,0.95,0.063,1.1510951854219846,1.0828776638271536,0.047,1.2867549199317392,1000
+Linear,Linear,experimental,True,6,0.9,0.888,0.9631437707318972,0.23828269404302604,0.885,1.107258053590484,1000
+Linear,Linear,experimental,True,6,0.95,0.9483333333333334,1.1476565887916024,0.23828269404302604,0.949,1.2877831442682166,1000
+Linear,Linear,observational,False,1,0.9,0.8906666666666666,0.2745349354396546,0.06791772384944492,0.877,0.35207829288672793,1000
+Linear,Linear,observational,False,1,0.95,0.94,0.3271285524396554,0.06791772384944492,0.927,0.3973383284293281,1000
+Linear,Linear,observational,False,4,0.9,0.17166666666666666,1.3453667150527824,1.069526476442094,0.148,1.5155288354203724,1000
+Linear,Linear,observational,False,4,0.95,0.2553333333333333,1.6031033183113808,1.069526476442094,0.227,1.76788776355175,1000
+Linear,Linear,observational,False,6,0.9,0.8876666666666666,1.0115696911437715,0.2500537801037969,0.885,1.1646692593828614,1000
+Linear,Linear,observational,False,6,0.95,0.947,1.2053596320109472,0.2500537801037969,0.945,1.3520516734344188,1000
+Linear,Linear,observational,True,1,0.9,0.8873333333333334,0.2723311152982007,0.06788449126443423,0.87,0.34906917904913193,1000
+Linear,Linear,observational,True,1,0.95,0.9333333333333333,0.32450253877200813,0.06788449126443423,0.922,0.39431076453237734,1000
+Linear,Linear,observational,True,4,0.9,0.16133333333333336,1.3452145611861124,1.0697056322506917,0.131,1.5155973414877166,1000
+Linear,Linear,observational,True,4,0.95,0.24866666666666665,1.6029220157967403,1.0697056322506917,0.221,1.7687727143970222,1000
+Linear,Linear,observational,True,6,0.9,0.8863333333333334,1.0032499571751785,0.24871625835196373,0.882,1.1544090742288704,1000
+Linear,Linear,observational,True,6,0.95,0.9433333333333334,1.1954460575309986,0.24871625835196373,0.939,1.340743500935446,1000
diff --git a/results/did/did_pa_multi_config.yml b/results/did/did_pa_multi_config.yml
index 944e713..fa87158 100644
--- a/results/did/did_pa_multi_config.yml
+++ b/results/did/did_pa_multi_config.yml
@@ -8,7 +8,7 @@ dgp_parameters:
   - 4
   - 6
   n_obs:
-  - 500
+  - 2000
 dml_parameters:
   in_sample_normalization:
   - true
@@ -61,12 +61,12 @@ dml_parameters:
       class_weight: null
       colsample_bytree: 1.0
       importance_type: split
-      learning_rate: 0.05
+      learning_rate: 0.02
       max_depth: -1
       min_child_samples: 20
       min_child_weight: 0.001
       min_split_gain: 0.0
-      n_estimators: 100
+      n_estimators: 500
       n_jobs: 1
       num_leaves: 31
       objective: null
@@ -97,12 +97,12 @@ dml_parameters:
       class_weight: null
       colsample_bytree: 1.0
       importance_type: split
-      learning_rate: 0.05
+      learning_rate: 0.02
       max_depth: -1
       min_child_samples: 20
       min_child_weight: 0.001
       min_split_gain: 0.0
-      n_estimators: 100
+      n_estimators: 500
       n_jobs: 1
       num_leaves: 31
       objective: null
diff --git a/scripts/did/did_pa_multi_config.yml b/scripts/did/did_pa_multi_config.yml
index 04f11a8..67eead1 100644
--- a/scripts/did/did_pa_multi_config.yml
+++ b/scripts/did/did_pa_multi_config.yml
@@ -8,7 +8,7 @@ simulation_parameters:
 
 dgp_parameters:
   DGP: [1, 4, 6] # Different DGP specifications
-  n_obs: [500] # Sample size for each simulation (has to be a list)
+  n_obs: [2000] # Sample size for each simulation (has to be a list)
 
 dml_parameters:
   # ML methods for ml_g and ml_m

From a092f304a13a99b1483d5d81cef8554f6803ce1b Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 31 Mar 2025 15:56:46 +0200
Subject: [PATCH 40/60] update workflow for uv

---
 .github/workflows/did_sim.yml | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index 75c3ffc..002fc3d 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -48,20 +48,24 @@ jobs:
       with:
         ref: ${{ env.TARGET_BRANCH }}
 
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        version: "0.6.11"
+
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.12'
+        python-version-file: "monte-cover/pyproject.toml"
 
-    - name: Install dependencies
+    - name: Install Monte-Cover
       run: |
-        python -m pip install --upgrade pip
-        pip install -r requirements.txt
+        uv sync
 
     - name: Install DoubleML from correct branch
       run: |
-        pip uninstall -y doubleml
-        pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
+        uv pip uninstall -y doubleml
+        uv pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
 
     - name: Set up Git configuration
       run: |
@@ -69,7 +73,7 @@ jobs:
         git config --global user.email 'github-actions@github.com'
 
     - name: Run scripts
-      run: python ${{ matrix.script }}
+      run: uv run python ${{ matrix.script }}
 
     - name: Commit any existing changes
       run: |

From 36f80aecaa17c93bb1f0e5981918016d0dd7e293 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 31 Mar 2025 15:59:14 +0200
Subject: [PATCH 41/60] add multi sim to script

---
 .github/workflows/did_sim.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index 002fc3d..005daed 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -19,6 +19,7 @@ jobs:
         script: [
           'scripts/did/did_pa_atte_coverage.py',
           'scripts/did/did_cs_atte_coverage.py',
+          'scripts/did/did_pa_multi.py',
         ]
 
     steps:

From 740277698e653e8f1d8959d73ea681768bb0c67d Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 31 Mar 2025 16:00:40 +0200
Subject: [PATCH 42/60] add monte-cover dir to workflow

---
 .github/workflows/did_sim.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index 005daed..09a6406 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -61,6 +61,7 @@ jobs:
 
     - name: Install Monte-Cover
       run: |
+        cd monte-cover
         uv sync
 
     - name: Install DoubleML from correct branch

From 388a3bd4613ffc6240bc84e54c3a251ccef77ed6 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 31 Mar 2025 16:03:58 +0200
Subject: [PATCH 43/60] fix pip uninstall flag

---
 .github/workflows/did_sim.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index 09a6406..1bd4f4b 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -66,7 +66,7 @@ jobs:
 
     - name: Install DoubleML from correct branch
       run: |
-        uv pip uninstall -y doubleml
+        uv pip uninstall doubleml
         uv pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
 
     - name: Set up Git configuration

From 8fa15bf4d45b0ee93baa0d32e874b1b8f140c0d3 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 31 Mar 2025 21:23:05 +0200
Subject: [PATCH 44/60] update uv sync dir

---
 .github/workflows/did_sim.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index 1bd4f4b..c931de5 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -61,8 +61,7 @@ jobs:
 
     - name: Install Monte-Cover
       run: |
-        cd monte-cover
-        uv sync
+        uv sync --config-file monte-cover/pyproject.toml
 
     - name: Install DoubleML from correct branch
       run: |

From 83799984b0b8f573845752a5f56052981247b843 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 31 Mar 2025 21:27:11 +0200
Subject: [PATCH 45/60] change to project arg

---
 .github/workflows/did_sim.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index c931de5..e7a5aff 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -61,7 +61,7 @@ jobs:
 
     - name: Install Monte-Cover
       run: |
-        uv sync --config-file monte-cover/pyproject.toml
+        uv sync --project monte-cover/pyproject.toml
 
     - name: Install DoubleML from correct branch
       run: |

From 40e7869f064b4ac6f19d6dd804b27b2bf811ad11 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 31 Mar 2025 21:30:18 +0200
Subject: [PATCH 46/60] update venv dir

---
 .github/workflows/did_sim.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index e7a5aff..b99e01e 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -61,7 +61,7 @@ jobs:
 
     - name: Install Monte-Cover
       run: |
-        uv sync --project monte-cover/pyproject.toml
+        uv sync --project monte-cover/pyproject.toml --venv .venv
 
     - name: Install DoubleML from correct branch
       run: |

From a1bb0f75d3d35452a362db435dee0f7d06c42324 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Tue, 1 Apr 2025 19:38:33 +0200
Subject: [PATCH 47/60] update workflow

---
 .github/workflows/did_sim.yml | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index b99e01e..b2fbcbd 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -61,12 +61,13 @@ jobs:
 
     - name: Install Monte-Cover
       run: |
-        uv sync --project monte-cover/pyproject.toml --venv .venv
+        uv venv --project monte-cover/pyproject.toml
+        uv sync --project monte-cover/pyproject.toml
 
     - name: Install DoubleML from correct branch
       run: |
-        uv pip uninstall doubleml
-        uv pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
+        uv pip uninstall --project monte-cover/pyproject.toml doubleml
+        uv pip install --project monte-cover/pyproject.toml "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
 
     - name: Set up Git configuration
       run: |
@@ -74,7 +75,7 @@ jobs:
         git config --global user.email 'github-actions@github.com'
 
     - name: Run scripts
-      run: uv run python ${{ matrix.script }}
+      run: uv run --project monte-cover/pyproject.toml ${{ matrix.script }}
 
     - name: Commit any existing changes
       run: |

From ada8c9453617b9331be5766c7893f81d779aa975 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Wed, 2 Apr 2025 20:59:02 +0200
Subject: [PATCH 48/60] update the paths for uv venv

---
 .github/workflows/did_sim.yml | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index b2fbcbd..c0dc522 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -61,11 +61,13 @@ jobs:
 
     - name: Install Monte-Cover
       run: |
-        uv venv --project monte-cover/pyproject.toml
-        uv sync --project monte-cover/pyproject.toml
+        cd monte-cover
+        uv venv
+        uv sync
 
     - name: Install DoubleML from correct branch
       run: |
+        source monte-cover/.venv/bin/activate
         uv pip uninstall --project monte-cover/pyproject.toml doubleml
         uv pip install --project monte-cover/pyproject.toml "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
 
@@ -75,7 +77,9 @@ jobs:
         git config --global user.email 'github-actions@github.com'
 
     - name: Run scripts
-      run: uv run --project monte-cover/pyproject.toml ${{ matrix.script }}
+      run: |
+        source monte-cover/.venv/bin/activate
+        uv run --project monte-cover/pyproject.toml ${{ matrix.script }}
 
     - name: Commit any existing changes
       run: |

From 98bc35b261fa69b829137bbc731ebfb9dd801bf7 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Thu, 3 Apr 2025 07:50:20 +0200
Subject: [PATCH 49/60] update script run

---
 .github/workflows/did_sim.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index c0dc522..159fdb6 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -79,7 +79,7 @@ jobs:
     - name: Run scripts
       run: |
         source monte-cover/.venv/bin/activate
-        uv run --project monte-cover/pyproject.toml ${{ matrix.script }}
+        uv run ${{ matrix.script }}
 
     - name: Commit any existing changes
       run: |

From c607b341fd85646c92cab3943da50ca9cbf75f7a Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Thu, 3 Apr 2025 08:50:23 +0200
Subject: [PATCH 50/60] update did and plr workflows

---
 .github/workflows/did_sim.yml |  4 ++--
 .github/workflows/plr_sim.yml | 23 ++++++++++++++++-------
 2 files changed, 18 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index 159fdb6..199220d 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -68,8 +68,8 @@ jobs:
     - name: Install DoubleML from correct branch
       run: |
         source monte-cover/.venv/bin/activate
-        uv pip uninstall --project monte-cover/pyproject.toml doubleml
-        uv pip install --project monte-cover/pyproject.toml "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
+        uv pip uninstall doubleml
+        uv pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
 
     - name: Set up Git configuration
       run: |
diff --git a/.github/workflows/plr_sim.yml b/.github/workflows/plr_sim.yml
index 7d95d3c..973a5b4 100644
--- a/.github/workflows/plr_sim.yml
+++ b/.github/workflows/plr_sim.yml
@@ -50,20 +50,27 @@ jobs:
       with:
         ref: ${{ env.TARGET_BRANCH }}
 
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        version: "0.6.11"
+  
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.12'
+        python-version-file: "monte-cover/pyproject.toml"
 
-    - name: Install dependencies
+    - name: Install Monte-Cover
       run: |
-        python -m pip install --upgrade pip
-        pip install -r requirements.txt
+        cd monte-cover
+        uv venv
+        uv sync
 
     - name: Install DoubleML from correct branch
       run: |
-        pip uninstall -y doubleml
-        pip install git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}
+        source monte-cover/.venv/bin/activate
+        uv pip uninstall doubleml
+        uv pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
 
     - name: Set up Git configuration
       run: |
@@ -71,7 +78,9 @@ jobs:
         git config --global user.email 'github-actions@github.com'
 
     - name: Run scripts
-      run: python ${{ matrix.script }}
+      run: |
+        source monte-cover/.venv/bin/activate
+        uv run ${{ matrix.script }}
 
     - name: Commit any existing changes
       run: |

From 72123353be77aa59520be126d402f1e16aea7266 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Thu, 24 Apr 2025 09:31:25 +0200
Subject: [PATCH 51/60] change import for did sim

---
 scripts/did/did_cs_atte_coverage.py | 2 +-
 scripts/did/did_pa_atte_coverage.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/did/did_cs_atte_coverage.py b/scripts/did/did_cs_atte_coverage.py
index 3886ea8..9be682b 100644
--- a/scripts/did/did_cs_atte_coverage.py
+++ b/scripts/did/did_cs_atte_coverage.py
@@ -7,7 +7,7 @@
 from lightgbm import LGBMRegressor, LGBMClassifier
 
 import doubleml as dml
-from doubleml.datasets import make_did_SZ2020
+from doubleml.did.datasets import make_did_SZ2020
 
 # Number of repetitions
 n_rep = 1000
diff --git a/scripts/did/did_pa_atte_coverage.py b/scripts/did/did_pa_atte_coverage.py
index 5184860..144597a 100644
--- a/scripts/did/did_pa_atte_coverage.py
+++ b/scripts/did/did_pa_atte_coverage.py
@@ -7,7 +7,7 @@
 from lightgbm import LGBMRegressor, LGBMClassifier
 
 import doubleml as dml
-from doubleml.datasets import make_did_SZ2020
+from doubleml.did.datasets import make_did_SZ2020
 
 # Number of repetitions
 n_rep = 1000

From 40aa5996ddff478f9a0ffce274bbc0c9e7062762 Mon Sep 17 00:00:00 2001
From: github-actions <github-actions@github.com>
Date: Thu, 24 Apr 2025 08:14:38 +0000
Subject: [PATCH 52/60] Update results from script:
 scripts/plm/plr_gate_coverage.py

---
 results/plm/plr_gate_coverage_metadata.csv | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/results/plm/plr_gate_coverage_metadata.csv b/results/plm/plr_gate_coverage_metadata.csv
index febe93f..980af1a 100644
--- a/results/plm/plr_gate_coverage_metadata.csv
+++ b/results/plm/plr_gate_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,plr_gate_coverage.py,2025-01-08 13:02:47,2835.5379569530487,3.12.8
+0.10.dev0,plr_gate_coverage.py,2025-04-24 08:14:37,1583.488787651062,3.12.3

From 4b0958587e62181749cc092d50e84b8e24c6f009 Mon Sep 17 00:00:00 2001
From: github-actions <github-actions@github.com>
Date: Thu, 24 Apr 2025 08:56:31 +0000
Subject: [PATCH 53/60] Update results from script:
 scripts/plm/plr_cate_coverage.py

---
 results/plm/plr_cate_coverage_metadata.csv | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/results/plm/plr_cate_coverage_metadata.csv b/results/plm/plr_cate_coverage_metadata.csv
index 59b490d..9e81be8 100644
--- a/results/plm/plr_cate_coverage_metadata.csv
+++ b/results/plm/plr_cate_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,plr_cate_coverage.py,2025-01-08 14:25:31,7808.868787527084,3.12.8
+0.10.dev0,plr_cate_coverage.py,2025-04-24 08:56:30,4092.604692697525,3.12.3

From 73dae7a01bf68298ab93cd95d2fb069e768a8129 Mon Sep 17 00:00:00 2001
From: github-actions <github-actions@github.com>
Date: Thu, 24 Apr 2025 09:36:44 +0000
Subject: [PATCH 54/60] Update results from script:
 scripts/plm/plr_ate_coverage.py

---
 results/plm/plr_ate_coverage.csv          | 38 ++++++++++-------------
 results/plm/plr_ate_coverage_metadata.csv |  2 +-
 2 files changed, 18 insertions(+), 22 deletions(-)

diff --git a/results/plm/plr_ate_coverage.csv b/results/plm/plr_ate_coverage.csv
index f801f58..1346d57 100644
--- a/results/plm/plr_ate_coverage.csv
+++ b/results/plm/plr_ate_coverage.csv
@@ -1,21 +1,17 @@
-Learner g,Learner m,Score,level,Coverage,CI Length,Bias,repetition
-LGBM,LGBM,IV-type,0.9,0.882,0.16071165852972294,0.04006345992726611,1000
-LGBM,LGBM,IV-type,0.95,0.938,0.19149975259363877,0.04006345992726611,1000
-LGBM,LGBM,partialling out,0.9,0.842,0.14721491277053025,0.042110152606416054,1000
-LGBM,LGBM,partialling out,0.95,0.906,0.17541738808225127,0.042110152606416054,1000
-Lasso,Lasso,IV-type,0.9,0.866,0.14050109787041962,0.03660923399902996,1000
-Lasso,Lasso,IV-type,0.95,0.915,0.16741738419894311,0.03660923399902996,1000
-Lasso,Lasso,partialling out,0.9,0.879,0.1474715771739181,0.03630990507775283,1000
-Lasso,Lasso,partialling out,0.95,0.931,0.17572322258236175,0.03630990507775283,1000
-Lasso,Random Forest,IV-type,0.9,0.886,0.1474535680840687,0.03770184842456424,1000
-Lasso,Random Forest,IV-type,0.95,0.939,0.17570176342823354,0.03770184842456424,1000
-Lasso,Random Forest,partialling out,0.9,0.785,0.14384571087180478,0.045773052530728796,1000
-Lasso,Random Forest,partialling out,0.95,0.883,0.17140273640142992,0.045773052530728796,1000
-Random Forest,Lasso,IV-type,0.9,0.879,0.14285466535066815,0.036599158846521436,1000
-Random Forest,Lasso,IV-type,0.95,0.93,0.17022183282640022,0.036599158846521436,1000
-Random Forest,Lasso,partialling out,0.9,0.892,0.15286076950172306,0.03825604774970179,1000
-Random Forest,Lasso,partialling out,0.95,0.939,0.1821448413180263,0.03825604774970179,1000
-Random Forest,Random Forest,IV-type,0.9,0.885,0.15036250130159673,0.037935837399518176,1000
-Random Forest,Random Forest,IV-type,0.95,0.944,0.17916797114809854,0.037935837399518176,1000
-Random Forest,Random Forest,partialling out,0.9,0.881,0.14718055225415835,0.03854156805613427,1000
-Random Forest,Random Forest,partialling out,0.95,0.934,0.17537644500167823,0.03854156805613427,1000
+Learner g,Learner m,score,level,Coverage,CI Length,Bias,repetition
+Lasso,Lasso,IV-type,0.9,0.881,0.1393979255576113,0.0352891099128789,1000
+Lasso,Lasso,IV-type,0.95,0.945,0.16610287331091153,0.0352891099128789,1000
+Lasso,Lasso,partialling out,0.9,0.908,0.14646362984437974,0.034686755904342816,1000
+Lasso,Lasso,partialling out,0.95,0.956,0.17452217926042807,0.034686755904342816,1000
+Lasso,Random Forest,IV-type,0.9,0.898,0.14672410325315619,0.036150217819390894,1000
+Lasso,Random Forest,IV-type,0.95,0.954,0.1748325524704008,0.036150217819390894,1000
+Lasso,Random Forest,partialling out,0.9,0.817,0.143330638432391,0.04191262539042403,1000
+Lasso,Random Forest,partialling out,0.95,0.886,0.17078898973477283,0.04191262539042403,1000
+Random Forest,Lasso,IV-type,0.9,0.877,0.141921407652651,0.035804568203505666,1000
+Random Forest,Lasso,IV-type,0.95,0.948,0.16910978768971624,0.035804568203505666,1000
+Random Forest,Lasso,partialling out,0.9,0.903,0.15198241550336503,0.03618790663815028,1000
+Random Forest,Lasso,partialling out,0.95,0.954,0.1810982179746172,0.03618790663815028,1000
+Random Forest,Random Forest,IV-type,0.9,0.907,0.1493755431098695,0.03649624902429271,1000
+Random Forest,Random Forest,IV-type,0.95,0.949,0.1779919379264572,0.03649624902429271,1000
+Random Forest,Random Forest,partialling out,0.9,0.884,0.14638253610723148,0.036967300943126565,1000
+Random Forest,Random Forest,partialling out,0.95,0.942,0.17442555011265587,0.036967300943126565,1000
diff --git a/results/plm/plr_ate_coverage_metadata.csv b/results/plm/plr_ate_coverage_metadata.csv
index 8d5c7fd..5d2418e 100644
--- a/results/plm/plr_ate_coverage_metadata.csv
+++ b/results/plm/plr_ate_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,plr_ate_coverage.py,2025-01-08 14:10:27,6916.502653360367,3.12.8
+0.10.dev0,plr_ate_coverage.py,2025-04-24 09:36:42,6508.21887755394,3.12.3

From 86b0edca9ca5bc20309257854fc747eff2c5344e Mon Sep 17 00:00:00 2001
From: github-actions <github-actions@github.com>
Date: Thu, 24 Apr 2025 10:27:16 +0000
Subject: [PATCH 55/60] Update results from script:
 scripts/plm/plr_ate_sensitivity.py

---
 results/plm/plr_ate_sensitivity.csv          | 26 ++++++++++----------
 results/plm/plr_ate_sensitivity_metadata.csv |  2 +-
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/results/plm/plr_ate_sensitivity.csv b/results/plm/plr_ate_sensitivity.csv
index 00934b4..7981ab2 100644
--- a/results/plm/plr_ate_sensitivity.csv
+++ b/results/plm/plr_ate_sensitivity.csv
@@ -1,17 +1,17 @@
 Learner g,Learner m,score,level,Coverage,CI Length,Bias,Coverage (Lower),Coverage (Upper),RV,RVa,Bias (Lower),Bias (Upper),repetition
 LGBM,LGBM,IV-type,0.9,0.49,1.2836841849471865,0.642852034975727,1.0,0.998,0.0883267598773138,0.025200860015212666,1.3451446703513257,0.27137365708627864,500
-LGBM,LGBM,IV-type,0.95,0.65,1.529604050351386,0.642852034975727,1.0,1.0,0.0883267598773138,0.013790413915027113,1.3451446703513257,0.27137365708627864,500
+LGBM,LGBM,IV-type,0.95,0.65,1.529604050351386,0.642852034975727,1.0,1.0,0.0883267598773138,0.013790413915027114,1.3451446703513257,0.27137365708627864,500
 LGBM,LGBM,partialling out,0.9,0.052,1.0555526522517718,0.9216896766797483,1.0,0.878,0.12290629723561408,0.0667859903596657,1.6463724503661545,0.2830593598175856,500
 LGBM,LGBM,partialling out,0.95,0.114,1.2577685626857555,0.9216896766797483,1.0,0.962,0.12290629723561408,0.05210975377693211,1.6463724503661545,0.2830593598175856,500
-LGBM,Random Forest,IV-type,0.9,0.072,1.1846157067784349,0.9322070273917288,1.0,0.934,0.11754241223304673,0.058515658188839345,1.6980814469560257,0.26726499739118403,500
-LGBM,Random Forest,IV-type,0.95,0.16,1.4115566776050241,0.9322070273917288,1.0,0.994,0.11754241223304673,0.04334326180178758,1.6980814469560257,0.26726499739118403,500
-LGBM,Random Forest,partialling out,0.9,0.078,1.2415314353647366,0.9950962660593035,1.0,0.918,0.11842149116467734,0.06021024644530299,1.8099982510012884,0.29229694861359934,500
-LGBM,Random Forest,partialling out,0.95,0.15,1.479375951220122,0.9950962660593035,1.0,0.98,0.11842149116467734,0.04525082945770369,1.8099982510012884,0.29229694861359934,500
-Random Forest,LGBM,IV-type,0.9,0.554,1.900486623597139,0.8881666888956794,1.0,1.0,0.07201385567825232,0.01814220891090863,2.11967639571274,0.46266796209857214,500
-Random Forest,LGBM,IV-type,0.95,0.746,2.2645694877143114,0.8881666888956794,1.0,1.0,0.07201385567825232,0.008578785415219068,2.11967639571274,0.46266796209857214,500
-Random Forest,LGBM,partialling out,0.9,0.002,1.5126486531347854,1.573242725840773,1.0,0.818,0.12824967172299567,0.08055028818351519,2.774465314925881,0.4031005519125824,500
-Random Forest,LGBM,partialling out,0.95,0.008,1.8024320418722997,1.573242725840773,1.0,0.948,0.12824967172299567,0.06705003584560411,2.774465314925881,0.4031005519125824,500
-Random Forest,Random Forest,IV-type,0.9,0.012,1.7129890288584426,1.6194855098925158,1.0,0.892,0.11959162432568951,0.07060214596391444,2.948463222220921,0.39969580126755805,500
-Random Forest,Random Forest,IV-type,0.95,0.038,2.041152323503278,1.6194855098925158,1.0,0.972,0.11959162432568951,0.05677408319758889,2.948463222220921,0.39969580126755805,500
-Random Forest,Random Forest,partialling out,0.9,0.0,1.7381117353695532,1.7366675645551972,1.0,0.824,0.1281371703134689,0.07833372270549209,3.0610973455175285,0.4635968018257515,500
-Random Forest,Random Forest,partialling out,0.95,0.018,2.0710878746970973,1.7366675645551972,1.0,0.946,0.1281371703134689,0.0642547167440049,3.0610973455175285,0.4635968018257515,500
+LGBM,Random Forest,IV-type,0.9,0.076,1.1831625247072215,0.9308062239804228,1.0,0.946,0.11734274159129685,0.05847446162225632,1.6971412413918896,0.26673626718612453,500
+LGBM,Random Forest,IV-type,0.95,0.15,1.40982510436599,0.9308062239804228,1.0,0.99,0.11734274159129685,0.04330052167462019,1.6971412413918896,0.26673626718612453,500
+LGBM,Random Forest,partialling out,0.9,0.074,1.2408824102177636,0.9969465002467008,1.0,0.93,0.11861197624692194,0.06042868914339051,1.8119621159758381,0.29323133175754634,500
+LGBM,Random Forest,partialling out,0.95,0.148,1.4786025900575936,0.9969465002467008,1.0,0.978,0.11861197624692194,0.04546020549453332,1.8119621159758381,0.29323133175754634,500
+Random Forest,LGBM,IV-type,0.9,0.556,1.9014670092481476,0.8897746033367344,1.0,1.0,0.07211930902619489,0.018402978138876647,2.1220916883044794,0.46567125428407596,500
+Random Forest,LGBM,IV-type,0.95,0.754,2.26573768927064,0.8897746033367344,1.0,1.0,0.07211930902619489,0.008809037433830246,2.1220916883044794,0.46567125428407596,500
+Random Forest,LGBM,partialling out,0.9,0.002,1.5148779135183594,1.5704287430339756,1.0,0.828,0.12800123432195906,0.0802383681199541,2.771929499160999,0.4014709332877519,500
+Random Forest,LGBM,partialling out,0.95,0.01,1.805088369458156,1.5704287430339756,1.0,0.942,0.12800123432195906,0.06673188982925375,2.771929499160999,0.4014709332877519,500
+Random Forest,Random Forest,IV-type,0.9,0.008,1.712740468910975,1.6062309195696611,1.0,0.896,0.11862651936611456,0.06965279743071341,2.9355183176965993,0.3908974740734249,500
+Random Forest,Random Forest,IV-type,0.95,0.042,2.040856146058031,1.6062309195696611,1.0,0.972,0.11862651936611456,0.05582930384773342,2.9355183176965993,0.3908974740734249,500
+Random Forest,Random Forest,partialling out,0.9,0.002,1.7398328179227547,1.738765052841725,1.0,0.826,0.12823635224819166,0.07839810366339917,3.0635620106955765,0.4647217397313531,500
+Random Forest,Random Forest,partialling out,0.95,0.016,2.0731386710496866,1.738765052841725,1.0,0.942,0.12823635224819166,0.06431405001184519,3.0635620106955765,0.4647217397313531,500
diff --git a/results/plm/plr_ate_sensitivity_metadata.csv b/results/plm/plr_ate_sensitivity_metadata.csv
index f0d1e74..18d033f 100644
--- a/results/plm/plr_ate_sensitivity_metadata.csv
+++ b/results/plm/plr_ate_sensitivity_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,plr_ate_sensitivity.py,2025-01-08 16:36:12,15659.224347829819,3.12.8
+0.10.dev0,plr_ate_sensitivity.py,2025-04-24 10:27:15,9539.846628665924,3.12.3

From eb1c8789daa8e1e60e6c2c02bf481577eca3bb5e Mon Sep 17 00:00:00 2001
From: github-actions <github-actions@github.com>
Date: Thu, 24 Apr 2025 10:30:20 +0000
Subject: [PATCH 56/60] Update results from script:
 scripts/did/did_pa_atte_coverage.py

---
 results/did/did_pa_atte_coverage.csv          | 48 +++++++++----------
 results/did/did_pa_atte_coverage_metadata.csv |  2 +-
 2 files changed, 25 insertions(+), 25 deletions(-)

diff --git a/results/did/did_pa_atte_coverage.csv b/results/did/did_pa_atte_coverage.csv
index 890866e..9119e34 100644
--- a/results/did/did_pa_atte_coverage.csv
+++ b/results/did/did_pa_atte_coverage.csv
@@ -1,16 +1,16 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,repetition
-LGBM,LGBM,experimental,False,1,0.9,0.051,2.162461753688773,2.169737683875206,1000
-LGBM,LGBM,experimental,False,1,0.95,0.094,2.576732109002647,2.169737683875206,1000
-LGBM,LGBM,experimental,False,2,0.9,0.377,2.1295044728560635,1.3213439323325022,1000
-LGBM,LGBM,experimental,False,2,0.95,0.475,2.5374610867049356,1.3213439323325022,1000
-LGBM,LGBM,experimental,False,3,0.9,0.47,1.8796286302073664,1.0014962629759643,1000
-LGBM,LGBM,experimental,False,3,0.95,0.584,2.239715655638386,1.0014962629759643,1000
-LGBM,LGBM,experimental,False,4,0.9,0.078,1.883011293050954,1.9173640236877347,1000
-LGBM,LGBM,experimental,False,4,0.95,0.122,2.243746347024319,1.9173640236877347,1000
-LGBM,LGBM,experimental,False,5,0.9,0.889,2.0642730548921726,0.5209994874770714,1000
-LGBM,LGBM,experimental,False,5,0.95,0.951,2.4597330580373264,0.5209994874770714,1000
-LGBM,LGBM,experimental,False,6,0.9,0.908,1.808638762699564,0.43376612813148785,1000
-LGBM,LGBM,experimental,False,6,0.95,0.948,2.155126011123672,0.43376612813148785,1000
+LGBM,LGBM,experimental,False,1,0.9,0.051,2.1624651397785515,2.1697505905302976,1000
+LGBM,LGBM,experimental,False,1,0.95,0.094,2.576736143777478,2.1697505905302976,1000
+LGBM,LGBM,experimental,False,2,0.9,0.378,2.1294819852681166,1.3213007503499328,1000
+LGBM,LGBM,experimental,False,2,0.95,0.475,2.537434291091179,1.3213007503499328,1000
+LGBM,LGBM,experimental,False,3,0.9,0.47,1.8796188347464011,1.0014962844281858,1000
+LGBM,LGBM,experimental,False,3,0.95,0.583,2.239703983626731,1.0014962844281858,1000
+LGBM,LGBM,experimental,False,4,0.9,0.078,1.8830006248174618,1.9173419242712182,1000
+LGBM,LGBM,experimental,False,4,0.95,0.122,2.243733635040054,1.9173419242712182,1000
+LGBM,LGBM,experimental,False,5,0.9,0.889,2.0642395778601936,0.5210006870170777,1000
+LGBM,LGBM,experimental,False,5,0.95,0.951,2.459693167693339,0.5210006870170777,1000
+LGBM,LGBM,experimental,False,6,0.9,0.908,1.8086231900602612,0.4337431389507495,1000
+LGBM,LGBM,experimental,False,6,0.95,0.948,2.1551074551794365,0.4337431389507495,1000
 LGBM,LGBM,experimental,True,1,0.9,0.049,2.159654648685151,2.17137681706751,1000
 LGBM,LGBM,experimental,True,1,0.95,0.099,2.573387237083486,2.17137681706751,1000
 LGBM,LGBM,experimental,True,2,0.9,0.373,2.129643042411216,1.319771507009933,1000
@@ -23,18 +23,18 @@ LGBM,LGBM,experimental,True,5,0.9,0.891,2.063666189492902,0.5200351889316859,100
 LGBM,LGBM,experimental,True,5,0.95,0.947,2.459009933312703,0.5200351889316859,1000
 LGBM,LGBM,experimental,True,6,0.9,0.897,1.8093537325759845,0.43669034939483387,1000
 LGBM,LGBM,experimental,True,6,0.95,0.944,2.1559779502779253,0.43669034939483387,1000
-LGBM,LGBM,observational,False,1,0.9,0.893,12.590352220156928,3.3786016989727083,1000
-LGBM,LGBM,observational,False,1,0.95,0.953,15.002329994503222,3.3786016989727083,1000
-LGBM,LGBM,observational,False,2,0.9,0.914,14.716998683865025,3.6220054403232314,1000
-LGBM,LGBM,observational,False,2,0.95,0.966,17.536385553259827,3.6220054403232314,1000
-LGBM,LGBM,observational,False,3,0.9,0.933,14.387779381623973,3.413493039888865,1000
-LGBM,LGBM,observational,False,3,0.95,0.977,17.144096558765057,3.413493039888865,1000
-LGBM,LGBM,observational,False,4,0.9,0.842,18.12890407178146,5.76469995432725,1000
-LGBM,LGBM,observational,False,4,0.95,0.932,21.60192157993247,5.76469995432725,1000
-LGBM,LGBM,observational,False,5,0.9,0.917,7.704461439047936,1.901354597911667,1000
-LGBM,LGBM,observational,False,5,0.95,0.96,9.18043204172422,1.901354597911667,1000
-LGBM,LGBM,observational,False,6,0.9,0.922,7.569503728862976,1.7987540722258704,1000
-LGBM,LGBM,observational,False,6,0.95,0.972,9.019620011362129,1.7987540722258704,1000
+LGBM,LGBM,observational,False,1,0.9,0.893,12.590652453419517,3.3787304616783773,1000
+LGBM,LGBM,observational,False,1,0.95,0.953,15.002687744501154,3.3787304616783773,1000
+LGBM,LGBM,observational,False,2,0.9,0.914,14.716645515368727,3.622038823656133,1000
+LGBM,LGBM,observational,False,2,0.95,0.966,17.535964727040476,3.622038823656133,1000
+LGBM,LGBM,observational,False,3,0.9,0.933,14.387879061625718,3.413516510279929,1000
+LGBM,LGBM,observational,False,3,0.95,0.977,17.14421533481309,3.413516510279929,1000
+LGBM,LGBM,observational,False,4,0.9,0.843,18.129751335736472,5.765050835726839,1000
+LGBM,LGBM,observational,False,4,0.95,0.932,21.602931157204278,5.765050835726839,1000
+LGBM,LGBM,observational,False,5,0.9,0.917,7.704465948378402,1.901185439754437,1000
+LGBM,LGBM,observational,False,5,0.95,0.96,9.180437414922883,1.901185439754437,1000
+LGBM,LGBM,observational,False,6,0.9,0.922,7.569553123736534,1.7987428999886972,1000
+LGBM,LGBM,observational,False,6,0.95,0.971,9.019678868984235,1.7987428999886972,1000
 LGBM,LGBM,observational,True,1,0.9,0.906,4.1677645580041025,1.0303464962408189,1000
 LGBM,LGBM,observational,True,1,0.95,0.967,4.966197779476664,1.0303464962408189,1000
 LGBM,LGBM,observational,True,2,0.9,0.917,5.020279353776863,1.2235227285733399,1000
diff --git a/results/did/did_pa_atte_coverage_metadata.csv b/results/did/did_pa_atte_coverage_metadata.csv
index 65f1786..1e59764 100644
--- a/results/did/did_pa_atte_coverage_metadata.csv
+++ b/results/did/did_pa_atte_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,did_pa_atte_coverage.py,2025-01-08 16:22:26,14811.1988697052,3.12.8
+0.10.dev0,did_pa_atte_coverage.py,2025-04-24 10:30:16,10597.874007225037,3.12.3

From ad393a562c3b33140c36089534d44db0859a3124 Mon Sep 17 00:00:00 2001
From: github-actions <github-actions@github.com>
Date: Thu, 24 Apr 2025 11:00:28 +0000
Subject: [PATCH 57/60] Update results from script:
 scripts/did/did_cs_atte_coverage.py

---
 results/did/did_cs_atte_coverage_metadata.csv | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/results/did/did_cs_atte_coverage_metadata.csv b/results/did/did_cs_atte_coverage_metadata.csv
index d6308e5..286a49b 100644
--- a/results/did/did_cs_atte_coverage_metadata.csv
+++ b/results/did/did_cs_atte_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,did_cs_atte_coverage.py,2025-01-08 16:52:06,16598.133431196213,3.12.8
+0.10.dev0,did_cs_atte_coverage.py,2025-04-24 11:00:22,12399.906484365463,3.12.3

From 14dfe0db546b4920ea3eedae5c1fff961ebbbdb1 Mon Sep 17 00:00:00 2001
From: github-actions <github-actions@github.com>
Date: Thu, 24 Apr 2025 13:16:12 +0000
Subject: [PATCH 58/60] Update results from script: scripts/did/did_pa_multi.py

---
 results/did/did_multi_detailed.csv   | 96 ++++++++++++++--------------
 results/did/did_multi_eventstudy.csv | 96 ++++++++++++++--------------
 results/did/did_multi_group.csv      | 96 ++++++++++++++--------------
 results/did/did_multi_metadata.csv   |  2 +-
 results/did/did_multi_time.csv       | 96 ++++++++++++++--------------
 5 files changed, 193 insertions(+), 193 deletions(-)

diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
index e584ef5..5b60263 100644
--- a/results/did/did_multi_detailed.csv
+++ b/results/did/did_multi_detailed.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-LGBM,LGBM,experimental,False,1,0.9,0.3929166666666667,0.6687029507520728,0.4569473540080381,0.066,1.0019963477166332,1000
-LGBM,LGBM,experimental,False,1,0.95,0.48991666666666667,0.7968087119452796,0.4569473540080381,0.131,1.1107772389821786,1000
-LGBM,LGBM,experimental,False,4,0.9,0.5314166666666666,0.5827863000707186,0.32927572636155183,0.206,0.8969333386170385,1000
-LGBM,LGBM,experimental,False,4,0.95,0.6208333333333333,0.6944327082397956,0.32927572636155183,0.298,0.9870085765972824,1000
-LGBM,LGBM,experimental,False,6,0.9,0.8985833333333334,0.5798291764546274,0.1415015522676616,0.902,0.8926496847901934,1000
-LGBM,LGBM,experimental,False,6,0.95,0.9505,0.6909090781183035,0.1415015522676616,0.94,0.9826119522997802,1000
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-LGBM,LGBM,experimental,True,1,0.95,0.49141666666666667,0.7965948092486183,0.4558614977424509,0.14,1.1116972038295265,1000
-LGBM,LGBM,experimental,True,4,0.9,0.529,0.5827218897275354,0.3290643441251289,0.21,0.8975401350497161,1000
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-LGBM,LGBM,experimental,True,6,0.95,0.9526666666666667,0.6909169583789544,0.14062961792513812,0.946,0.9820497847451054,1000
-LGBM,LGBM,observational,False,1,0.9,0.91325,2.7335893497080668,0.7032100492915742,0.97,4.24966855110796,1000
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-Linear,Linear,experimental,False,6,0.9,0.89825,0.9829577849827824,0.24024745112751242,0.91,1.4202537375432176,1000
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-Linear,Linear,experimental,True,4,0.95,0.3835,1.1626765261043008,0.819502522016252,0.065,1.5757772289647831,1000
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diff --git a/results/did/did_multi_eventstudy.csv b/results/did/did_multi_eventstudy.csv
index 43df8e7..4fa42e2 100644
--- a/results/did/did_multi_eventstudy.csv
+++ b/results/did/did_multi_eventstudy.csv
@@ -1,49 +1,49 @@
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diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
index c3ea832..62f4f74 100644
--- a/results/did/did_multi_group.csv
+++ b/results/did/did_multi_group.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
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-LGBM,LGBM,observational,True,4,0.95,0.97,1.701894510454578,0.3144435220940553,0.976,2.0500099983055438,1000
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diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
index cf6d79f..afcf3e3 100644
--- a/results/did/did_multi_metadata.csv
+++ b/results/did/did_multi_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.10.dev0,DIDMultiCoverageSimulation,2025-03-22 13:07,161.55725783109665,3.12.9,scripts/did/did_pa_multi_config.yml
+0.10.dev0,DIDMultiCoverageSimulation,2025-04-24 13:16,342.5480104605357,3.12.3,scripts/did/did_pa_multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
index 69a4f0a..366e2db 100644
--- a/results/did/did_multi_time.csv
+++ b/results/did/did_multi_time.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
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-Linear,Linear,observational,False,6,0.9,0.8876666666666666,1.0115696911437715,0.2500537801037969,0.885,1.1646692593828614,1000
-Linear,Linear,observational,False,6,0.95,0.947,1.2053596320109472,0.2500537801037969,0.945,1.3520516734344188,1000
-Linear,Linear,observational,True,1,0.9,0.8873333333333334,0.2723311152982007,0.06788449126443423,0.87,0.34906917904913193,1000
-Linear,Linear,observational,True,1,0.95,0.9333333333333333,0.32450253877200813,0.06788449126443423,0.922,0.39431076453237734,1000
-Linear,Linear,observational,True,4,0.9,0.16133333333333336,1.3452145611861124,1.0697056322506917,0.131,1.5155973414877166,1000
-Linear,Linear,observational,True,4,0.95,0.24866666666666665,1.6029220157967403,1.0697056322506917,0.221,1.7687727143970222,1000
-Linear,Linear,observational,True,6,0.9,0.8863333333333334,1.0032499571751785,0.24871625835196373,0.882,1.1544090742288704,1000
-Linear,Linear,observational,True,6,0.95,0.9433333333333334,1.1954460575309986,0.24871625835196373,0.939,1.340743500935446,1000
+LGBM,LGBM,experimental,False,1,0.9,0.0892018779342723,0.6735044298830777,0.5847473306131643,0.06103286384976526,0.8011157200032835,213
+LGBM,LGBM,experimental,False,1,0.95,0.17214397496087636,0.8025300272131505,0.5847473306131643,0.11267605633802817,0.9212869785087825,213
+LGBM,LGBM,experimental,False,4,0.9,0.26917057902973396,0.5421756711901682,0.40575619933105894,0.22065727699530516,0.6593899758823104,213
+LGBM,LGBM,experimental,False,4,0.95,0.3630672926447574,0.6460421592625465,0.40575619933105894,0.27230046948356806,0.7560532967515187,213
+LGBM,LGBM,experimental,False,6,0.9,0.9061032863849765,0.5400510579454025,0.1320131828976933,0.8967136150234741,0.655954994913595,213
+LGBM,LGBM,experimental,False,6,0.95,0.9436619718309859,0.6435105264335905,0.1320131828976933,0.9530516431924883,0.7541743667823617,213
+LGBM,LGBM,experimental,True,1,0.9,0.10172143974960875,0.6733529287914019,0.5852776399276569,0.06103286384976526,0.7967235061979042,213
+LGBM,LGBM,experimental,True,1,0.95,0.17370892018779344,0.8023495025278913,0.5852776399276569,0.13145539906103287,0.9244790434903511,213
+LGBM,LGBM,experimental,True,4,0.9,0.27699530516431925,0.5420776526750094,0.40698586915558177,0.19718309859154928,0.6590010475339207,213
+LGBM,LGBM,experimental,True,4,0.95,0.36619718309859156,0.6459253629978932,0.40698586915558177,0.29107981220657275,0.756935199686189,213
+LGBM,LGBM,experimental,True,6,0.9,0.9092331768388106,0.5402460899427872,0.13118228677637048,0.9342723004694836,0.656709876900506,213
+LGBM,LGBM,experimental,True,6,0.95,0.9624413145539906,0.6437429213922929,0.13118228677637048,0.9577464788732394,0.752708121867163,213
+LGBM,LGBM,observational,False,1,0.9,0.9092331768388106,2.949178578913003,0.7288513967921139,0.9389671361502347,3.640414629214853,213
+LGBM,LGBM,observational,False,1,0.95,0.971830985915493,3.5141630257760554,0.7288513967921139,0.971830985915493,4.150557782712354,213
+LGBM,LGBM,observational,False,4,0.9,0.9014084507042254,3.8964402739946777,1.1223719380630706,0.9530516431924883,4.764333689391444,213
+LGBM,LGBM,observational,False,4,0.95,0.9671361502347418,4.6428949541819975,1.1223719380630706,0.971830985915493,5.471006129563335,213
+LGBM,LGBM,observational,False,6,0.9,0.9327073552425664,2.0285442450363886,0.4469362530691951,0.9436619718309859,2.5322168766535476,213
+LGBM,LGBM,observational,False,6,0.95,0.974960876369327,2.4171595552159215,0.4469362530691951,0.971830985915493,2.88178088183908,213
+LGBM,LGBM,observational,True,1,0.9,0.9311424100156495,1.123106120095932,0.26198849206178,0.9483568075117371,1.3900521401677945,213
+LGBM,LGBM,observational,True,1,0.95,0.9812206572769953,1.3382634844440697,0.26198849206178,0.971830985915493,1.590522570297692,213
+LGBM,LGBM,observational,True,4,0.9,0.9389671361502347,1.5058133543184269,0.3406931806329066,0.9389671361502347,1.8423134951538827,213
+LGBM,LGBM,observational,True,4,0.95,0.974960876369327,1.794287280974358,0.3406931806329066,0.9765258215962441,2.1083548286609535,213
+LGBM,LGBM,observational,True,6,0.9,0.9295774647887324,0.9567250856892477,0.21575142072047634,0.9248826291079812,1.1904853742941865,213
+LGBM,LGBM,observational,True,6,0.95,0.9687010954616588,1.1400082538239402,0.21575142072047634,0.9624413145539906,1.3586919640085193,213
+Linear,Linear,experimental,False,1,0.9,0.8372456964006261,0.24439003484018698,0.06945083196171528,0.7934272300469484,0.3129341078438043,213
+Linear,Linear,experimental,False,1,0.95,0.9061032863849765,0.29120868788489906,0.06945083196171528,0.8967136150234741,0.3539477153716639,213
+Linear,Linear,experimental,False,4,0.9,0.0297339593114241,0.9657374450457906,1.10215836325763,0.018779342723004695,1.1083961248126262,213
+Linear,Linear,experimental,False,4,0.95,0.05007824726134585,1.1507471423579276,1.10215836325763,0.03755868544600939,1.2889435969715142,213
+Linear,Linear,experimental,False,6,0.9,0.8826291079812206,0.9660570269958759,0.24916879933420294,0.863849765258216,1.1099601757570683,213
+Linear,Linear,experimental,False,6,0.95,0.9405320813771518,1.1511279477390344,0.24916879933420294,0.9483568075117371,1.2907822410705019,213
+Linear,Linear,experimental,True,1,0.9,0.8435054773082942,0.2443806338560857,0.06938368490846683,0.7981220657276995,0.31301675619249564,213
+Linear,Linear,experimental,True,1,0.95,0.9061032863849765,0.2911974859214199,0.06938368490846683,0.892018779342723,0.3540111034488695,213
+Linear,Linear,experimental,True,4,0.9,0.028169014084507043,0.9656711081516489,1.101877793396912,0.018779342723004695,1.10638268902308,213
+Linear,Linear,experimental,True,4,0.95,0.051643192488262914,1.1506680970731475,1.101877793396912,0.03755868544600939,1.2854015212203158,213
+Linear,Linear,experimental,True,6,0.9,0.8826291079812206,0.9661738823391016,0.24920436172242275,0.8826291079812206,1.109718677746468,213
+Linear,Linear,experimental,True,6,0.95,0.9499217527386542,1.1512671894687365,0.24920436172242275,0.9436619718309859,1.289880357441484,213
+Linear,Linear,observational,False,1,0.9,0.92018779342723,0.2751888265519432,0.06174150416456478,0.92018779342723,0.35292004587977904,213
+Linear,Linear,observational,False,1,0.95,0.9530516431924883,0.32790771175747974,0.06174150416456478,0.971830985915493,0.3984225349256207,213
+Linear,Linear,observational,False,4,0.9,0.14241001564945227,1.3371389555418136,1.0786049675340639,0.11737089201877934,1.5095232758334527,213
+Linear,Linear,observational,False,4,0.95,0.2331768388106416,1.5932993381573268,1.0786049675340639,0.19248826291079812,1.7603486086872133,213
+Linear,Linear,observational,False,6,0.9,0.892018779342723,1.0174596865123355,0.2598926764856041,0.863849765258216,1.169235918197709,213
+Linear,Linear,observational,False,6,0.95,0.9389671361502347,1.2123779943760464,0.2598926764856041,0.9483568075117371,1.3563715660755367,213
+Linear,Linear,observational,True,1,0.9,0.9186228482003129,0.27330760979256297,0.06141517644317234,0.9154929577464789,0.3497979849970347,213
+Linear,Linear,observational,True,1,0.95,0.9577464788732394,0.32566610372921273,0.06141517644317234,0.9577464788732394,0.39565988623663145,213
+Linear,Linear,observational,True,4,0.9,0.12206572769953052,1.3286076657019632,1.085838237023361,0.09389671361502347,1.496899245381295,213
+Linear,Linear,observational,True,4,0.95,0.19874804381846634,1.5831336793085395,1.085838237023361,0.19248826291079812,1.74994500832392,213
+Linear,Linear,observational,True,6,0.9,0.8857589984350547,1.0121360659183458,0.2604339364788449,0.8779342723004695,1.1644370852327328,213
+Linear,Linear,observational,True,6,0.95,0.9389671361502347,1.2060345091803977,0.2604339364788449,0.9530516431924883,1.3523232762515875,213

From fbf89754ff22f53e2421f24fbd3e0e5950243567 Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 28 Apr 2025 10:31:41 +0200
Subject: [PATCH 59/60] update plr results

---
 monte-cover/src/montecover/plm/plr_ate.py |  4 +-
 results/plm/plr_ate_config.yml            | 86 ++++++++++++++++++-----
 results/plm/plr_ate_coverage.csv          | 42 ++++++-----
 results/plm/plr_ate_metadata.csv          |  2 +-
 scripts/plm/plr_ate_config.yml            |  2 +
 5 files changed, 100 insertions(+), 36 deletions(-)

diff --git a/monte-cover/src/montecover/plm/plr_ate.py b/monte-cover/src/montecover/plm/plr_ate.py
index a58b471..ac7f1c3 100644
--- a/monte-cover/src/montecover/plm/plr_ate.py
+++ b/monte-cover/src/montecover/plm/plr_ate.py
@@ -49,9 +49,9 @@ def _convert_ml_string_to_object(self, ml_string):
         if ml_string == "Lasso":
             learner = LassoCV()
         elif ml_string == "Random Forest":
-            learner = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
+            learner = RandomForestRegressor(n_estimators=200, max_features=10, max_depth=5, min_samples_leaf=20)
         elif ml_string == "LGBM":
-            learner = LGBMRegressor(n_estimators=100, learning_rate=0.05, verbose=-1, n_jobs=1)
+            learner = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1, n_jobs=1)
         else:
             raise ValueError(f"Unknown learner type: {ml_string}")
 
diff --git a/results/plm/plr_ate_config.yml b/results/plm/plr_ate_config.yml
index 18c7f83..af73b18 100644
--- a/results/plm/plr_ate_config.yml
+++ b/results/plm/plr_ate_config.yml
@@ -83,15 +83,15 @@ dml_parameters:
       - ccp_alpha
       - monotonic_cst
       max_depth: 5
-      max_features: 20
+      max_features: 10
       max_leaf_nodes: null
       max_samples: null
       min_impurity_decrease: 0.0
-      min_samples_leaf: 2
+      min_samples_leaf: 20
       min_samples_split: 2
       min_weight_fraction_leaf: 0.0
       monotonic_cst: null
-      n_estimators: 100
+      n_estimators: 200
       n_jobs: null
       oob_score: false
       random_state: null
@@ -122,15 +122,15 @@ dml_parameters:
         splitter: best
       estimator_params: *id001
       max_depth: 5
-      max_features: 20
+      max_features: 10
       max_leaf_nodes: null
       max_samples: null
       min_impurity_decrease: 0.0
-      min_samples_leaf: 2
+      min_samples_leaf: 20
       min_samples_split: 2
       min_weight_fraction_leaf: 0.0
       monotonic_cst: null
-      n_estimators: 100
+      n_estimators: 200
       n_jobs: null
       oob_score: false
       random_state: null
@@ -179,15 +179,15 @@ dml_parameters:
         splitter: best
       estimator_params: *id001
       max_depth: 5
-      max_features: 20
+      max_features: 10
       max_leaf_nodes: null
       max_samples: null
       min_impurity_decrease: 0.0
-      min_samples_leaf: 2
+      min_samples_leaf: 20
       min_samples_split: 2
       min_weight_fraction_leaf: 0.0
       monotonic_cst: null
-      n_estimators: 100
+      n_estimators: 200
       n_jobs: null
       oob_score: false
       random_state: null
@@ -218,15 +218,15 @@ dml_parameters:
         splitter: best
       estimator_params: *id001
       max_depth: 5
-      max_features: 20
+      max_features: 10
       max_leaf_nodes: null
       max_samples: null
       min_impurity_decrease: 0.0
-      min_samples_leaf: 2
+      min_samples_leaf: 20
       min_samples_split: 2
       min_weight_fraction_leaf: 0.0
       monotonic_cst: null
-      n_estimators: 100
+      n_estimators: 200
       n_jobs: null
       oob_score: false
       random_state: null
@@ -270,12 +270,12 @@ dml_parameters:
       class_weight: null
       colsample_bytree: 1.0
       importance_type: split
-      learning_rate: 0.05
+      learning_rate: 0.01
       max_depth: -1
       min_child_samples: 20
       min_child_weight: 0.001
       min_split_gain: 0.0
-      n_estimators: 100
+      n_estimators: 500
       n_jobs: 1
       num_leaves: 31
       objective: null
@@ -306,12 +306,12 @@ dml_parameters:
       class_weight: null
       colsample_bytree: 1.0
       importance_type: split
-      learning_rate: 0.05
+      learning_rate: 0.01
       max_depth: -1
       min_child_samples: 20
       min_child_weight: 0.001
       min_split_gain: 0.0
-      n_estimators: 100
+      n_estimators: 500
       n_jobs: 1
       num_leaves: 31
       objective: null
@@ -322,6 +322,60 @@ dml_parameters:
       subsample_for_bin: 200000
       subsample_freq: 0
       verbose: -1
+  - ml_g: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMRegressor
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.01
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 500
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+    ml_m: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
   score:
   - partialling out
   - IV-type
diff --git a/results/plm/plr_ate_coverage.csv b/results/plm/plr_ate_coverage.csv
index 1346d57..87a505d 100644
--- a/results/plm/plr_ate_coverage.csv
+++ b/results/plm/plr_ate_coverage.csv
@@ -1,17 +1,25 @@
-Learner g,Learner m,score,level,Coverage,CI Length,Bias,repetition
-Lasso,Lasso,IV-type,0.9,0.881,0.1393979255576113,0.0352891099128789,1000
-Lasso,Lasso,IV-type,0.95,0.945,0.16610287331091153,0.0352891099128789,1000
-Lasso,Lasso,partialling out,0.9,0.908,0.14646362984437974,0.034686755904342816,1000
-Lasso,Lasso,partialling out,0.95,0.956,0.17452217926042807,0.034686755904342816,1000
-Lasso,Random Forest,IV-type,0.9,0.898,0.14672410325315619,0.036150217819390894,1000
-Lasso,Random Forest,IV-type,0.95,0.954,0.1748325524704008,0.036150217819390894,1000
-Lasso,Random Forest,partialling out,0.9,0.817,0.143330638432391,0.04191262539042403,1000
-Lasso,Random Forest,partialling out,0.95,0.886,0.17078898973477283,0.04191262539042403,1000
-Random Forest,Lasso,IV-type,0.9,0.877,0.141921407652651,0.035804568203505666,1000
-Random Forest,Lasso,IV-type,0.95,0.948,0.16910978768971624,0.035804568203505666,1000
-Random Forest,Lasso,partialling out,0.9,0.903,0.15198241550336503,0.03618790663815028,1000
-Random Forest,Lasso,partialling out,0.95,0.954,0.1810982179746172,0.03618790663815028,1000
-Random Forest,Random Forest,IV-type,0.9,0.907,0.1493755431098695,0.03649624902429271,1000
-Random Forest,Random Forest,IV-type,0.95,0.949,0.1779919379264572,0.03649624902429271,1000
-Random Forest,Random Forest,partialling out,0.9,0.884,0.14638253610723148,0.036967300943126565,1000
-Random Forest,Random Forest,partialling out,0.95,0.942,0.17442555011265587,0.036967300943126565,1000
+Learner g,Learner m,Score,level,Coverage,CI Length,Bias,repetition
+LGBM,LGBM,IV-type,0.9,0.873,0.1600979705787658,0.041736453670531866,1000
+LGBM,LGBM,IV-type,0.95,0.937,0.19076849829726017,0.041736453670531866,1000
+LGBM,LGBM,partialling out,0.9,0.807,0.14701046434404016,0.043407336971130486,1000
+LGBM,LGBM,partialling out,0.95,0.887,0.17517377275621215,0.043407336971130486,1000
+LGBM,Lasso,IV-type,0.9,0.878,0.14908437061246727,0.03862236976140604,1000
+LGBM,Lasso,IV-type,0.95,0.926,0.17764498449616647,0.03862236976140604,1000
+LGBM,Lasso,partialling out,0.9,0.892,0.15981114790450912,0.039529461666836616,1000
+LGBM,Lasso,partialling out,0.95,0.945,0.19042672800093632,0.039529461666836616,1000
+Lasso,Lasso,IV-type,0.9,0.877,0.1404646476549414,0.03601132554422347,1000
+Lasso,Lasso,IV-type,0.95,0.928,0.16737395108830316,0.03601132554422347,1000
+Lasso,Lasso,partialling out,0.9,0.9,0.14736731076791165,0.03607425067346851,1000
+Lasso,Lasso,partialling out,0.95,0.938,0.17559898149657663,0.03607425067346851,1000
+Lasso,Random Forest,IV-type,0.9,0.839,0.13067912086907693,0.03663040619086667,1000
+Lasso,Random Forest,IV-type,0.95,0.901,0.1557137767385692,0.03663040619086667,1000
+Lasso,Random Forest,partialling out,0.9,0.753,0.14303542962976423,0.04845002977336067,1000
+Lasso,Random Forest,partialling out,0.95,0.854,0.17043722675016001,0.04845002977336067,1000
+Random Forest,Lasso,IV-type,0.9,0.883,0.14162830043619654,0.03632778967870182,1000
+Random Forest,Lasso,IV-type,0.95,0.933,0.16876052889948315,0.03632778967870182,1000
+Random Forest,Lasso,partialling out,0.9,0.888,0.1512642580778129,0.037711493804103186,1000
+Random Forest,Lasso,partialling out,0.95,0.948,0.18024248062130577,0.037711493804103186,1000
+Random Forest,Random Forest,IV-type,0.9,0.852,0.13195953745670919,0.036614704598155204,1000
+Random Forest,Random Forest,IV-type,0.95,0.911,0.15723948720656877,0.036614704598155204,1000
+Random Forest,Random Forest,partialling out,0.9,0.896,0.14282075762578683,0.03605962387417277,1000
+Random Forest,Random Forest,partialling out,0.95,0.94,0.17018142928016597,0.03605962387417277,1000
diff --git a/results/plm/plr_ate_metadata.csv b/results/plm/plr_ate_metadata.csv
index b96ed32..16eb5fb 100644
--- a/results/plm/plr_ate_metadata.csv
+++ b/results/plm/plr_ate_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.10.dev0,PLRATECoverageSimulation,2025-03-21 17:43,12.395700534184774,3.12.9,scripts/plm/plr_ate_config.yml
+0.10.dev0,PLRATECoverageSimulation,2025-04-28 10:01,17.98821387688319,3.12.9,scripts/plm/plr_ate_config.yml
diff --git a/scripts/plm/plr_ate_config.yml b/scripts/plm/plr_ate_config.yml
index 93ee95c..e2067bc 100644
--- a/scripts/plm/plr_ate_config.yml
+++ b/scripts/plm/plr_ate_config.yml
@@ -24,6 +24,8 @@ dml_parameters:
       ml_m: ["Lasso"]
     - ml_g: ["LGBM"]
       ml_m: ["LGBM"]
+    - ml_g: ["LGBM"]
+      ml_m: ["Lasso"]
 
   score: ["partialling out", "IV-type"]
 

From b9dac9161b562358a99d6176b50057f9276ca24c Mon Sep 17 00:00:00 2001
From: SvenKlaassen <sven.klaassen@uni-hamburg.de>
Date: Mon, 28 Apr 2025 13:18:20 +0200
Subject: [PATCH 60/60] rerun did multi sim

---
 doc/did/did_multi.qmd                | 21 +++++-
 results/did/did_multi_detailed.csv   | 96 ++++++++++++++--------------
 results/did/did_multi_eventstudy.csv | 96 ++++++++++++++--------------
 results/did/did_multi_group.csv      | 96 ++++++++++++++--------------
 results/did/did_multi_metadata.csv   |  2 +-
 results/did/did_multi_time.csv       | 96 ++++++++++++++--------------
 6 files changed, 213 insertions(+), 194 deletions(-)

diff --git a/doc/did/did_multi.qmd b/doc/did/did_multi.qmd
index ef05bda..0cfed2e 100644
--- a/doc/did/did_multi.qmd
+++ b/doc/did/did_multi.qmd
@@ -62,7 +62,13 @@ def make_pretty(df, level, n_rep):
 
 ## ATTE Coverage
 
-The simulations are based on the  the [make_did_SZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_did_SZ2020.html)-DGP with $1000$ observations. Learners are only set to boosting, due to time constraints (and the nonlinearity of some of the DGPs).
+The simulations are based on the  the [make_did_CS2021](https://docs.doubleml.org/dev/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs:
+
+ - Type 1: Linear outcome model and treatment assignment
+ - Type 4: Nonlinear outcome model and treatment assignment
+ - Type 6: Randomized treatment assignment and nonlinear outcome model
+
+The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). 
 
 ::: {.callout-note title="Metadata"  collapse="true"}
 
@@ -110,6 +116,8 @@ make_pretty(df_ate_9, level, n_rep)
 
 ### Experimental Score
 
+The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
+
 ```{python}
 #| echo: false
 score = "experimental"
@@ -130,6 +138,10 @@ make_pretty(df_ate_9, level, n_rep)
 
 ## Aggregated Effects
 
+These simulations test different types of aggregation, as described in [DiD User Guide](https://docs.doubleml.org/dev/guide/models.html#difference-in-differences-models-did).
+
+The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). 
+
 ### Group Effects
 
 ```{python}
@@ -166,6 +178,8 @@ make_pretty(df_ate_9, level, n_rep)
 
 #### Experimental Score
 
+The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
+
 ```{python}
 #| echo: false
 score = "experimental"
@@ -220,6 +234,8 @@ make_pretty(df_ate_9, level, n_rep)
 
 #### Experimental Score
 
+The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
+
 ```{python}
 #| echo: false
 score = "experimental"
@@ -274,6 +290,9 @@ make_pretty(df_ate_9, level, n_rep)
 
 #### Experimental Score
 
+The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
+
+
 ```{python}
 #| echo: false
 score = "experimental"
diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
index 5b60263..4b6d870 100644
--- a/results/did/did_multi_detailed.csv
+++ b/results/did/did_multi_detailed.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-LGBM,LGBM,experimental,False,1,0.9,0.40532081377151796,0.6677253898222073,0.45051314831034145,0.08450704225352113,1.0005469053604612,213
-LGBM,LGBM,experimental,False,1,0.95,0.5007824726134585,0.7956438762517957,0.45051314831034145,0.15492957746478872,1.1095167204679879,213
-LGBM,LGBM,experimental,False,4,0.9,0.548904538341158,0.5814196300250507,0.3237627211845255,0.24413145539906103,0.8948945048168284,213
-LGBM,LGBM,experimental,False,4,0.95,0.6381064162754303,0.6928042204373742,0.3237627211845255,0.3333333333333333,0.9852933278894489,213
-LGBM,LGBM,experimental,False,6,0.9,0.8974960876369327,0.5798375844479007,0.14088912748925403,0.8685446009389671,0.8915452790934116,213
-LGBM,LGBM,experimental,False,6,0.95,0.9464006259780908,0.6909190968602316,0.14088912748925403,0.9436619718309859,0.9825229360947381,213
-LGBM,LGBM,experimental,True,1,0.9,0.4123630672926447,0.6676885234807931,0.4519262895879074,0.06103286384976526,1.0019752546929692,213
-LGBM,LGBM,experimental,True,1,0.95,0.5066510172143975,0.7955999472965198,0.4519262895879074,0.1596244131455399,1.1110236525840373,213
-LGBM,LGBM,experimental,True,4,0.9,0.548904538341158,0.5813589039575363,0.3235983019273521,0.22065727699530516,0.8953221615501692,213
-LGBM,LGBM,experimental,True,4,0.95,0.63302034428795,0.6927318608648866,0.3235983019273521,0.3333333333333333,0.9865207873010946,213
-LGBM,LGBM,experimental,True,6,0.9,0.8959311424100157,0.5799660631056092,0.14000216987912564,0.892018779342723,0.892440611190863,213
-LGBM,LGBM,experimental,True,6,0.95,0.9471830985915493,0.6910721886233918,0.14000216987912564,0.9389671361502347,0.9816012226010797,213
-LGBM,LGBM,observational,False,1,0.9,0.9080594679186228,2.7888919178439227,0.7035607423224498,0.9483568075117371,4.334054524286509,213
-LGBM,LGBM,observational,False,1,0.95,0.9636150234741784,3.3231696888918334,0.7035607423224498,0.971830985915493,4.758567521024827,213
-LGBM,LGBM,observational,False,4,0.9,0.8986697965571204,3.4956058606970917,0.9669737969666872,0.9624413145539906,5.377062497216683,213
-LGBM,LGBM,observational,False,4,0.95,0.9643974960876369,4.165271291532112,0.9669737969666872,0.9859154929577465,5.921434465221159,213
-LGBM,LGBM,observational,False,6,0.9,0.9225352112676056,2.190208929252288,0.5167519307047893,0.9671361502347418,3.4154051265476033,213
-LGBM,LGBM,observational,False,6,0.95,0.969092331768388,2.6097949079569784,0.5167519307047893,0.9859154929577465,3.7483564185778286,213
-LGBM,LGBM,observational,True,1,0.9,0.9064945226917058,1.1368726933892221,0.2783026371207721,0.9389671361502347,1.7717588062983114,213
-LGBM,LGBM,observational,True,1,0.95,0.9620500782472613,1.3546673682932289,0.2783026371207721,0.9671361502347418,1.9429676563761076,213
-LGBM,LGBM,observational,True,4,0.9,0.9158841940532082,1.4254308754317884,0.33866283883400894,0.9014084507042254,2.202675700642339,213
-LGBM,LGBM,observational,True,4,0.95,0.9616588419405321,1.6985056496945854,0.33866283883400894,0.9530516431924883,2.421762282833961,213
-LGBM,LGBM,observational,True,6,0.9,0.9151017214397495,1.0463068305990617,0.24826243692564173,0.92018779342723,1.636975223567781,213
-LGBM,LGBM,observational,True,6,0.95,0.9604851330203443,1.246751486667643,0.24826243692564173,0.9765258215962441,1.7956888077887063,213
-Linear,Linear,experimental,False,1,0.9,0.8615023474178404,0.29480782482194634,0.0786421880740863,0.812206572769953,0.45940764438954385,213
-Linear,Linear,experimental,False,1,0.95,0.926056338028169,0.3512851900886226,0.0786421880740863,0.8826291079812206,0.5045899270443839,213
-Linear,Linear,experimental,False,4,0.9,0.28482003129890454,0.975894733280616,0.8330978501200553,0.018779342723004695,1.412718271492504,213
-Linear,Linear,experimental,False,4,0.95,0.3658059467918623,1.1628502977965955,0.8330978501200553,0.07511737089201878,1.575365705490123,213
-Linear,Linear,experimental,False,6,0.9,0.9049295774647887,0.9845361877515872,0.23869850791621902,0.8967136150234741,1.4224342348752608,213
-Linear,Linear,experimental,False,6,0.95,0.9483568075117371,1.17314722589988,0.23869850791621902,0.9530516431924883,1.5861522128436303,213
-Linear,Linear,experimental,True,1,0.9,0.8630672926447575,0.2947851759266125,0.07861158483452843,0.812206572769953,0.4597746703234241,213
-Linear,Linear,experimental,True,1,0.95,0.9264475743348983,0.35125820226525856,0.07861158483452843,0.892018779342723,0.5049192522960325,213
-Linear,Linear,experimental,True,4,0.9,0.2895148669796557,0.9759084550161325,0.8326923121552289,0.023474178403755867,1.4121969723711594,213
-Linear,Linear,experimental,True,4,0.95,0.365414710485133,1.1628666482529382,0.8326923121552289,0.07042253521126761,1.5761780385299982,213
-Linear,Linear,experimental,True,6,0.9,0.8986697965571204,0.9845444925351251,0.23872704179656282,0.892018779342723,1.4218091878908978,213
-Linear,Linear,experimental,True,6,0.95,0.9483568075117371,1.1731571216598229,0.23872704179656282,0.9483568075117371,1.5857684237069227,213
-Linear,Linear,observational,False,1,0.9,0.9119718309859155,0.31913264481306997,0.07504846382409917,0.9530516431924883,0.4966577096852585,213
-Linear,Linear,observational,False,1,0.95,0.9581377151799687,0.3802700008534462,0.07504846382409917,0.971830985915493,0.5457965923475415,213
-Linear,Linear,observational,False,4,0.9,0.4061032863849765,1.2274527144295637,0.8093030821575489,0.17370892018779344,1.7566478372130891,213
-Linear,Linear,observational,False,4,0.95,0.5093896713615024,1.4626001205144619,0.8093030821575489,0.27699530516431925,1.9631223954139048,213
-Linear,Linear,observational,False,6,0.9,0.9021909233176838,1.0316221276603512,0.24917990085463324,0.9061032863849765,1.488738970418394,213
-Linear,Linear,observational,False,6,0.95,0.954225352112676,1.2292535838683007,0.24917990085463324,0.9342723004694836,1.6622175036443294,213
-Linear,Linear,observational,True,1,0.9,0.9092331768388106,0.3171256867784898,0.07481042183312375,0.9248826291079812,0.493585500375777,213
-Linear,Linear,observational,True,1,0.95,0.9585289514866979,0.377878562854461,0.07481042183312375,0.9671361502347418,0.5423776539443926,213
-Linear,Linear,observational,True,4,0.9,0.3974960876369327,1.2240896932129774,0.8120340183488837,0.16901408450704225,1.7523471284937546,213
-Linear,Linear,observational,True,4,0.95,0.5003912363067292,1.4585928335706568,0.8120340183488837,0.27230046948356806,1.9580510608715704,213
-Linear,Linear,observational,True,6,0.9,0.9025821596244131,1.0267883142861962,0.25013308990604005,0.8967136150234741,1.4811458752504516,213
-Linear,Linear,observational,True,6,0.95,0.9503129890453834,1.223493739973321,0.25013308990604005,0.9483568075117371,1.6532118883170046,213
+LGBM,LGBM,experimental,False,1,0.9,0.4075,0.6680449671616123,0.4536971478292392,0.106,1.0014645995766545,1000
+LGBM,LGBM,experimental,False,1,0.95,0.4969166666666667,0.796024676138938,0.4536971478292392,0.149,1.1101910211253438,1000
+LGBM,LGBM,experimental,False,4,0.9,0.5299166666666666,0.5832956540561086,0.33011501262366927,0.221,0.8979294468476701,1000
+LGBM,LGBM,experimental,False,4,0.95,0.6176666666666666,0.695039640948207,0.33011501262366927,0.302,0.9884075405086947,1000
+LGBM,LGBM,experimental,False,6,0.9,0.89775,0.5802322315200278,0.1421029922499711,0.891,0.8927725918322601,1000
+LGBM,LGBM,experimental,False,6,0.95,0.9469166666666666,0.69138934785114,0.1421029922499711,0.948,0.9827083539024966,1000
+LGBM,LGBM,experimental,True,1,0.9,0.4046666666666667,0.6678639885704151,0.45277355982980805,0.085,1.0010411944316,1000
+LGBM,LGBM,experimental,True,1,0.95,0.4968333333333333,0.7958090268465587,0.45277355982980805,0.143,1.1099170693556415,1000
+LGBM,LGBM,experimental,True,4,0.9,0.5294166666666666,0.5833160278609535,0.32982215913570107,0.212,0.8981881991531053,1000
+LGBM,LGBM,experimental,True,4,0.95,0.6166666666666666,0.6950639178340466,0.32982215913570107,0.297,0.9891633208675981,1000
+LGBM,LGBM,experimental,True,6,0.9,0.89975,0.580193622985627,0.14218113073558258,0.901,0.8924214431904512,1000
+LGBM,LGBM,experimental,True,6,0.95,0.9490833333333334,0.691343342944879,0.14218113073558258,0.941,0.9821856487700115,1000
+LGBM,LGBM,observational,False,1,0.9,0.9114166666666667,2.745442581507895,0.7067512182439725,0.956,4.267995889646912,1000
+LGBM,LGBM,observational,False,1,0.95,0.9645833333333333,3.271396611351424,0.7067512182439725,0.987,4.685018272931908,1000
+LGBM,LGBM,observational,False,4,0.9,0.90725,3.522756602113769,0.964041513740829,0.962,5.420041663733542,1000
+LGBM,LGBM,observational,False,4,0.95,0.9638333333333333,4.197623395365736,0.964041513740829,0.992,5.964308172387913,1000
+LGBM,LGBM,observational,False,6,0.9,0.9243333333333333,2.1873521785120444,0.5149849766795307,0.961,3.4127245573450833,1000
+LGBM,LGBM,observational,False,6,0.95,0.96975,2.6063908794939334,0.5149849766795307,0.983,3.744841077573908,1000
+LGBM,LGBM,observational,True,1,0.9,0.9086666666666666,1.1201454805349786,0.27275770285583456,0.928,1.746888265672863,1000
+LGBM,LGBM,observational,True,1,0.95,0.9575833333333333,1.334735664815871,0.27275770285583456,0.973,1.916447134945182,1000
+LGBM,LGBM,observational,True,4,0.9,0.9208333333333334,1.415823117191095,0.3279182075028663,0.928,2.1893552276764523,1000
+LGBM,LGBM,observational,True,4,0.95,0.9615833333333333,1.6870572996314692,0.3279182075028663,0.968,2.408008384167633,1000
+LGBM,LGBM,observational,True,6,0.9,0.9035833333333334,1.0342009611787182,0.25272266117158226,0.925,1.6169600815502123,1000
+LGBM,LGBM,observational,True,6,0.95,0.9551666666666666,1.2323264535360365,0.25272266117158226,0.979,1.7730857540946194,1000
+Linear,Linear,experimental,False,1,0.9,0.8538333333333333,0.29474516889634966,0.08084244811337131,0.777,0.45978521833536623,1000
+Linear,Linear,experimental,False,1,0.95,0.9159166666666666,0.3512105309483955,0.08084244811337131,0.87,0.5045503638584217,1000
+Linear,Linear,experimental,False,4,0.9,0.3103333333333333,0.9755748479295066,0.8059915763717771,0.042,1.4116936904642545,1000
+Linear,Linear,experimental,False,4,0.95,0.3874166666666667,1.16246913089087,0.8059915763717771,0.078,1.5738462873560166,1000
+Linear,Linear,experimental,False,6,0.9,0.905,0.9844757965076787,0.23586020264982355,0.904,1.421142814988169,1000
+Linear,Linear,experimental,False,6,0.95,0.9513333333333334,1.1730752652943266,0.23586020264982355,0.957,1.5854008375388589,1000
+Linear,Linear,experimental,True,1,0.9,0.8529166666666667,0.2947409209064057,0.08090529833241722,0.773,0.4595811381839562,1000
+Linear,Linear,experimental,True,1,0.95,0.9155,0.3512054691561725,0.08090529833241722,0.873,0.5044085624001035,1000
+Linear,Linear,experimental,True,4,0.9,0.30975,0.9755981107617252,0.8057657027853717,0.045,1.4127941326439093,1000
+Linear,Linear,experimental,True,4,0.95,0.38825,1.1624968502651534,0.8057657027853717,0.082,1.5745211647752397,1000
+Linear,Linear,experimental,True,6,0.9,0.90425,0.9845527716230539,0.23562373204232834,0.902,1.4213288951702785,1000
+Linear,Linear,experimental,True,6,0.95,0.9529166666666666,1.1731669868015593,0.23562373204232834,0.954,1.5855821588385934,1000
+Linear,Linear,observational,False,1,0.9,0.9013333333333333,0.3180945635163335,0.07688429889402026,0.893,0.4947430553763157,1000
+Linear,Linear,observational,False,1,0.95,0.9528333333333334,0.379033050694909,0.07688429889402026,0.939,0.543298878262958,1000
+Linear,Linear,observational,False,4,0.9,0.41125,1.2376859429116744,0.7929800095241654,0.179,1.7688886396548633,1000
+Linear,Linear,observational,False,4,0.95,0.517,1.4747937643389757,0.7929800095241654,0.271,1.9773822754488526,1000
+Linear,Linear,observational,False,6,0.9,0.9018333333333334,1.0288570593763655,0.2510929759491061,0.907,1.4841286685672213,1000
+Linear,Linear,observational,False,6,0.95,0.951,1.225958801790062,0.2510929759491061,0.952,1.6571596022563468,1000
+Linear,Linear,observational,True,1,0.9,0.9031666666666667,0.31609423050346813,0.07696990405262741,0.896,0.4919409315123034,1000
+Linear,Linear,observational,True,1,0.95,0.951,0.3766495068962009,0.07696990405262741,0.938,0.540169288119527,1000
+Linear,Linear,observational,True,4,0.9,0.4071666666666667,1.2320142323935404,0.7928492798685264,0.177,1.7614082912050193,1000
+Linear,Linear,observational,True,4,0.95,0.5160833333333333,1.4680355044159439,0.7928492798685264,0.27,1.9699921213258018,1000
+Linear,Linear,observational,True,6,0.9,0.8995833333333334,1.0226446037500223,0.25004589301052516,0.908,1.4775056202619612,1000
+Linear,Linear,observational,True,6,0.95,0.9493333333333334,1.2185562043286982,0.25004589301052516,0.958,1.6496021973448636,1000
diff --git a/results/did/did_multi_eventstudy.csv b/results/did/did_multi_eventstudy.csv
index 4fa42e2..001f0d3 100644
--- a/results/did/did_multi_eventstudy.csv
+++ b/results/did/did_multi_eventstudy.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
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diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
index 62f4f74..4577e7d 100644
--- a/results/did/did_multi_group.csv
+++ b/results/did/did_multi_group.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
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diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
index afcf3e3..7bdadd5 100644
--- a/results/did/did_multi_metadata.csv
+++ b/results/did/did_multi_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.10.dev0,DIDMultiCoverageSimulation,2025-04-24 13:16,342.5480104605357,3.12.3,scripts/did/did_pa_multi_config.yml
+0.10.dev0,DIDMultiCoverageSimulation,2025-04-28 13:09,156.835364429156,3.12.9,scripts/did/did_pa_multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
index 366e2db..5602827 100644
--- a/results/did/did_multi_time.csv
+++ b/results/did/did_multi_time.csv
@@ -1,49 +1,49 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
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