diff --git a/tests/testthat/test-double_ml_pliv_partial_functional_initializer_IVtype.R b/tests/testthat/test-double_ml_pliv_partial_functional_initializer_IVtype.R
new file mode 100644
index 00000000..1955b8ab
--- /dev/null
+++ b/tests/testthat/test-double_ml_pliv_partial_functional_initializer_IVtype.R
@@ -0,0 +1,63 @@
+context("Unit tests for PLIV, partialling out X, Z, XZ")
+
+lgr::get_logger("mlr3")$set_threshold("warn")
+
+on_cran = !identical(Sys.getenv("NOT_CRAN"), "true")
+if (on_cran) {
+  test_cases = expand.grid(
+    learner = "regr.lm",
+    dml_procedure = "dml2",
+    score = "IV-type",
+    stringsAsFactors = FALSE)
+} else {
+  test_cases = expand.grid(
+    learner = c("regr.lm", "regr.cv_glmnet"),
+    dml_procedure = c("dml1", "dml2"),
+    score = "IV-type",
+    stringsAsFactors = FALSE)
+}
+test_cases[".test_name"] = apply(test_cases, 1, paste, collapse = "_")
+
+patrick::with_parameters_test_that("Unit tests for PLIV (partialX functional initialization):",
+  .cases = test_cases, {
+    learner_pars = get_default_mlmethod_pliv(learner)
+    df = data_pliv$df
+    Xnames = names(df)[names(df) %in% c("y", "d", "z", "z2") == FALSE]
+    data_ml = double_ml_data_from_data_frame(df,
+                                             y_col = "y",
+                                             d_cols = "d", x_cols = Xnames, z_cols = "z")
+    
+    # Partial out X (default PLIV)
+    set.seed(3141)
+    double_mlpliv_obj = DoubleMLPLIV$new(data_ml,
+                                         n_folds = 5,
+                                         ml_l = learner_pars$ml_l$clone(),
+                                         ml_m = learner_pars$ml_m$clone(),
+                                         ml_r = learner_pars$ml_r$clone(),
+                                         ml_g = learner_pars$ml_g$clone(),
+                                         dml_procedure = dml_procedure,
+                                         score = score)
+    
+    double_mlpliv_obj$fit()
+    theta_obj = double_mlpliv_obj$coef
+    se_obj = double_mlpliv_obj$se
+    
+    # Partial out X
+    set.seed(3141)
+    double_mlpliv_partX = DoubleMLPLIV.partialX(data_ml,
+                                                n_folds = 5,
+                                                ml_l = learner_pars$ml_l$clone(),
+                                                ml_m = learner_pars$ml_m$clone(),
+                                                ml_r = learner_pars$ml_r$clone(),
+                                                ml_g = learner_pars$ml_g$clone(),
+                                                dml_procedure = dml_procedure,
+                                                score = score)
+  
+  double_mlpliv_partX$fit()
+  theta_partX = double_mlpliv_partX$coef
+  se_partX = double_mlpliv_partX$se
+  
+  expect_equal(theta_partX, theta_obj, tolerance = 1e-8)
+  expect_equal(se_partX, se_obj, tolerance = 1e-8)
+  }
+)