diff --git a/doc/api/dml_models.rst b/doc/api/dml_models.rst
index 11481640..f8adaf06 100644
--- a/doc/api/dml_models.rst
+++ b/doc/api/dml_models.rst
@@ -17,6 +17,7 @@ doubleml.plm
 
     DoubleMLPLR
     DoubleMLPLIV
+    DoubleMLLPLR
 
 
 .. _api_irm_models:
diff --git a/doc/guide/models/plm/lplr.rst b/doc/guide/models/plm/lplr.rst
new file mode 100644
index 00000000..3bb7d146
--- /dev/null
+++ b/doc/guide/models/plm/lplr.rst
@@ -0,0 +1,13 @@
+**Logistic partially linear regression (LPLR)** models take the form
+
+.. math::
+
+    \mathbb{E} [Y | D, X] = \mathbb{P} (Y=1 | D, X) = \text{expit} \{\beta_0 D + r_0 (X) \}
+
+where :math:`Y` is the binary outcome variable and :math:`D` is the policy variable of interest.
+The high-dimensional vector :math:`X = (X_1, \ldots, X_p)` consists of other confounding covariates.
+:math:`\text{expit}` is the logistic link function
+
+.. math::
+    \text{expit} ( X ) = \frac{1}{1 + e^{-x}}
+
diff --git a/doc/guide/models/plm/plm_models.inc b/doc/guide/models/plm/plm_models.inc
index 851944fe..ebac3ef5 100644
--- a/doc/guide/models/plm/plm_models.inc
+++ b/doc/guide/models/plm/plm_models.inc
@@ -120,4 +120,39 @@ Estimation is conducted via its ``fit()`` method:
             obj_dml_data = DoubleMLData$new(data, y_col="y", d_col = "d", z_cols= "Z1")
             dml_pliv_obj = DoubleMLPLIV$new(obj_dml_data, ml_l, ml_m, ml_r)
             dml_pliv_obj$fit()
-            print(dml_pliv_obj)
\ No newline at end of file
+            print(dml_pliv_obj)
+
+Logistic partially linear regression model (LPLR)
+***************************************
+
+.. include:: /guide/models/plm/lplr.rst
+
+.. include:: /shared/causal_graphs/plr_irm_causal_graph.rst
+
+``DoubleMLLPLR`` implements LPLR models. Estimation is conducted via its ``fit()`` method.
+
+.. note::
+    Remark that the treatment effects are not additive in this model. The partial linear term enters the model through a logistic link function.
+
+.. tab-set::
+
+    .. tab-item:: Python
+        :sync: py
+
+        .. ipython:: python
+
+            import numpy as np
+            import doubleml as dml
+            from doubleml.plm.datasets import make_lplr_LZZ2020
+            from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+            from sklearn.base import clone
+            np.random.seed(3141)
+            ml_t = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
+            ml_m = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
+            ml_M = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
+            obj_dml_data = make_lplr_LZZ2020(alpha=0.5, n_obs=500, dim_x=20)
+            dml_lplr_obj = dml.DoubleMLPLR(obj_dml_data, ml_M, ml_t, ml_m)
+            dml_lplr_obj.fit().summary
+
+
+