add kwargs to cate and gate irm

doubleml/irm/irm.py

@@ -431,7 +431,7 @@ def _nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_
 
         return res
 
-    def cate(self, basis, is_gate=False):
+    def cate(self, basis, is_gate=False, **kwargs):
         """
         Calculate conditional average treatment effects (CATE) for a given basis.
 
@@ -440,10 +440,14 @@ def cate(self, basis, is_gate=False):
         basis : :class:`pandas.DataFrame`
             The basis for estimating the best linear predictor. Has to have the shape ``(n_obs, d)``,
             where ``n_obs`` is the number of observations and ``d`` is the number of predictors.
+
         is_gate : bool
             Indicates whether the basis is constructed for GATEs (dummy-basis).
             Default is ``False``.
 
+        **kwargs: dict
+            Additional keyword arguments to be passed to :meth:`statsmodels.regression.linear_model.OLS.fit` e.g. ``cov_type``.
+
         Returns
         -------
         model : :class:`doubleML.DoubleMLBLP`
@@ -462,10 +466,10 @@ def cate(self, basis, is_gate=False):
         orth_signal = self.psi_elements['psi_b'].reshape(-1)
         # fit the best linear predictor
         model = DoubleMLBLP(orth_signal, basis=basis, is_gate=is_gate)
-        model.fit()
+        model.fit(**kwargs)
         return model
 
-    def gate(self, groups):
+    def gate(self, groups, **kwargs):
         """
         Calculate group average treatment effects (GATE) for groups.
 
@@ -476,6 +480,9 @@ def gate(self, groups):
             Has to be dummy coded with shape ``(n_obs, d)``, where ``n_obs`` is the number of observations
             and ``d`` is the number of groups or ``(n_obs, 1)`` and contain the corresponding groups (as str).
 
+        **kwargs: dict
+            Additional keyword arguments to be passed to :meth:`statsmodels.regression.linear_model.OLS.fit` e.g. ``cov_type``.
+
         Returns
         -------
         model : :class:`doubleML.DoubleMLBLP`
@@ -495,7 +502,7 @@ def gate(self, groups):
         if any(groups.sum(0) <= 5):
             warnings.warn('At least one group effect is estimated with less than 6 observations.')
 
-        model = self.cate(groups, is_gate=True)
+        model = self.cate(groups, is_gate=True, **kwargs)
         return model
 
     def policy_tree(self, features, depth=2, **tree_params):

doubleml/irm/tests/test_irm.py

@@ -187,8 +187,14 @@ def test_dml_irm_sensitivity_rho0(dml_irm_fixture):
                        rtol=1e-9, atol=1e-4)
 
 
+@pytest.fixture(scope='module',
+                params=["nonrobust", "HC0", "HC1", "HC2", "HC3"])
+def cov_type(request):
+    return request.param
+
+
 @pytest.mark.ci
-def test_dml_irm_cate_gate():
+def test_dml_irm_cate_gate(cov_type):
     n = 9
     # collect data
     np.random.seed(42)
@@ -207,7 +213,7 @@ def test_dml_irm_cate_gate():
     dml_irm_obj.fit()
     # create a random basis
     random_basis = pd.DataFrame(np.random.normal(0, 1, size=(n, 5)))
-    cate = dml_irm_obj.cate(random_basis)
+    cate = dml_irm_obj.cate(random_basis, cov_type=cov_type)
     assert isinstance(cate, dml.utils.blp.DoubleMLBLP)
     assert isinstance(cate.confint(), pd.DataFrame)
 
@@ -216,7 +222,7 @@ def test_dml_irm_cate_gate():
                             columns=['Group 1', 'Group 2'])
     msg = ('At least one group effect is estimated with less than 6 observations.')
     with pytest.warns(UserWarning, match=msg):
-        gate_1 = dml_irm_obj.gate(groups_1)
+        gate_1 = dml_irm_obj.gate(groups_1, cov_type=cov_type)
     assert isinstance(gate_1, dml.utils.blp.DoubleMLBLP)
     assert isinstance(gate_1.confint(), pd.DataFrame)
     assert all(gate_1.confint().index == groups_1.columns.to_list())
@@ -225,7 +231,7 @@ def test_dml_irm_cate_gate():
     groups_2 = pd.DataFrame(np.random.choice(["1", "2"], n))
     msg = ('At least one group effect is estimated with less than 6 observations.')
     with pytest.warns(UserWarning, match=msg):
-        gate_2 = dml_irm_obj.gate(groups_2)
+        gate_2 = dml_irm_obj.gate(groups_2, cov_type=cov_type)
     assert isinstance(gate_2, dml.utils.blp.DoubleMLBLP)
     assert isinstance(gate_2.confint(), pd.DataFrame)
     assert all(gate_2.confint().index == ["Group_1", "Group_2"])