Remove deprecated positional arguments

econml/_ortho_learner.py

@@ -543,10 +543,8 @@ def _prefit(self, Y, T, *args, only_final=False, **kwargs):
 
         super()._prefit(Y, T, *args, **kwargs)
 
-    @_deprecate_positional("X, W, and Z should be passed by keyword only. In a future release "
-                           "we will disallow passing X, W, and Z by position.", ['X', 'W', 'Z'])
     @BaseCateEstimator._wrap_fit
-    def fit(self, Y, T, X=None, W=None, Z=None, *, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, Z=None, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
             cache_values=False, inference=None, only_final=False, check_input=True):
         """
         Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.

econml/dml/_rlearner.py

@@ -326,9 +326,7 @@ def _gen_ortho_learner_model_nuisance(self):
     def _gen_ortho_learner_model_final(self):
         return _ModelFinal(self._gen_rlearner_model_final())
 
-    @_deprecate_positional("X, and should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
             cache_values=False, inference=None):
         """
         Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.

econml/dml/causal_forest.py

@@ -704,10 +704,7 @@ def tune(self, Y, T, *, X=None, W=None,
         return self
 
     # override only so that we can update the docstring to indicate support for `blb`
-
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).

econml/dml/dml.py

@@ -461,9 +461,7 @@ def _gen_rlearner_model_final(self):
         return _FinalWrapper(self._gen_model_final(), self.fit_cate_intercept, self._gen_featurizer(), False)
 
     # override only so that we can update the docstring to indicate support for `LinearModelFinalInference`
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
@@ -615,9 +613,7 @@ def _gen_model_final(self):
         return StatsModelsLinearRegression(fit_intercept=False)
 
     # override only so that we can update the docstring to indicate support for `StatsModelsInference`
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
@@ -826,9 +822,7 @@ def _gen_model_final(self):
                                         n_jobs=self.n_jobs,
                                         random_state=self.random_state)
 
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
@@ -1157,9 +1151,7 @@ def _gen_rlearner_model_final(self):
 
     # override only so that we can update the docstring to indicate
     # support for `GenericSingleTreatmentModelFinalInference`
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).

econml/dr/_drlearner.py

@@ -455,9 +455,7 @@ def _gen_model_final(self):
     def _gen_ortho_learner_model_final(self):
         return _ModelFinal(self._gen_model_final(), self._gen_featurizer(), self.multitask_model_final)
 
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.
@@ -849,9 +847,7 @@ def _gen_model_final(self):
     def _gen_ortho_learner_model_final(self):
         return _ModelFinal(self._gen_model_final(), self._gen_featurizer(), False)
 
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight=None, sample_var=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.
@@ -1144,9 +1140,7 @@ def _gen_model_final(self):
     def _gen_ortho_learner_model_final(self):
         return _ModelFinal(self._gen_model_final(), self._gen_featurizer(), False)
 
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.
@@ -1440,9 +1434,7 @@ def _gen_model_final(self):
     def _gen_ortho_learner_model_final(self):
         return _ModelFinal(self._gen_model_final(), self._gen_featurizer(), False)
 
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, groups=None,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None,
             cache_values=False, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).

econml/dynamic/dml/_dml.py

@@ -540,9 +540,7 @@ def _strata(self, Y, T, X=None, W=None, Z=None,
         # Required for bootstrap inference
         return groups
 
-    @_deprecate_positional("X, and should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups,
+    def fit(self, Y, T, *, X=None, W=None, sample_weight=None, sample_var=None, groups,
             cache_values=False, inference='auto'):
         """Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.
 

econml/iv/nnet/_deepiv.py

@@ -296,10 +296,8 @@ def __init__(self, *,
         self._second_stage_options = second_stage_options
         super().__init__()
 
-    @_deprecate_positional("X and Z should be passed by keyword only. In a future release "
-                           "we will disallow passing X and Z by position.", ['X', 'Z'])
     @BaseCateEstimator._wrap_fit
-    def fit(self, Y, T, X, Z, *, inference=None):
+    def fit(self, Y, T, *, X, Z, inference=None):
         """Estimate the counterfactual model from data.
 
         That is, estimate functions τ(·, ·, ·), ∂τ(·, ·).

econml/iv/sieve/_tsls.py

@@ -208,10 +208,8 @@ def __init__(self, *,
         self._model_Y = LinearRegression(fit_intercept=False)
         super().__init__()
 
-    @_deprecate_positional("X, W, and Z should be passed by keyword only. In a future release "
-                           "we will disallow passing X, W, and Z by position.", ['X', 'W', 'Z'])
     @BaseCateEstimator._wrap_fit
-    def fit(self, Y, T, Z, X=None, W=None, *, inference=None):
+    def fit(self, Y, T, *, Z, X=None, W=None, inference=None):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·, ·, ·), ∂τ(·, ·).
 

econml/metalearners/_metalearners.py

@@ -42,10 +42,8 @@ def __init__(self, *,
         self.categories = categories
         super().__init__()
 
-    @_deprecate_positional("X should be passed by keyword only. In a future release "
-                           "we will disallow passing X by position.", ['X'])
     @BaseCateEstimator._wrap_fit
-    def fit(self, Y, T, X, *, inference=None):
+    def fit(self, Y, T, *, X, inference=None):
         """Build an instance of TLearner.
 
         Parameters
@@ -131,10 +129,8 @@ def __init__(self, *,
         self.categories = categories
         super().__init__()
 
-    @_deprecate_positional("X should be passed by keyword only. In a future release "
-                           "we will disallow passing X by position.", ['X'])
     @BaseCateEstimator._wrap_fit
-    def fit(self, Y, T, X=None, *, inference=None):
+    def fit(self, Y, T, *, X=None, inference=None):
         """Build an instance of SLearner.
 
         Parameters
@@ -240,10 +236,8 @@ def __init__(self, *,
         self.categories = categories
         super().__init__()
 
-    @_deprecate_positional("X should be passed by keyword only. In a future release "
-                           "we will disallow passing X by position.", ['X'])
     @BaseCateEstimator._wrap_fit
-    def fit(self, Y, T, X, *, inference=None):
+    def fit(self, Y, T, *, X, inference=None):
         """Build an instance of XLearner.
 
         Parameters
@@ -367,10 +361,8 @@ def __init__(self, *,
         self.categories = categories
         super().__init__()
 
-    @_deprecate_positional("X should be passed by keyword only. In a future release "
-                           "we will disallow passing X by position.", ['X'])
     @BaseCateEstimator._wrap_fit
-    def fit(self, Y, T, X, *, inference=None):
+    def fit(self, Y, T, *, X, inference=None):
         """Build an instance of DomainAdaptationLearner.
 
         Parameters

econml/orf/_ortho_forest.py

@@ -250,10 +250,8 @@ def __init__(self,
         self.categories = categories
         super().__init__()
 
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
     @BaseCateEstimator._wrap_fit
-    def fit(self, Y, T, X, W=None, *, inference='auto'):
+    def fit(self, Y, T, *, X, W=None, inference='auto'):
         """Build an orthogonal random forest from a training set (Y, T, X, W).
 
         Parameters
@@ -610,9 +608,7 @@ def _combine(self, X, W):
 
     # Need to redefine fit here for auto inference to work due to a quirk in how
     # wrap_fit is defined
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X, W=None, *, inference='auto'):
+    def fit(self, Y, T, *, X, W=None, inference='auto'):
         """Build an orthogonal random forest from a training set (Y, T, X, W).
 
         Parameters
@@ -949,9 +945,7 @@ def __init__(self, *,
             batch_size=batch_size,
             random_state=self.random_state)
 
-    @_deprecate_positional("X and W should be passed by keyword only. In a future release "
-                           "we will disallow passing X and W by position.", ['X', 'W'])
-    def fit(self, Y, T, X, W=None, *, inference='auto'):
+    def fit(self, Y, T, *, X, W=None, inference='auto'):
         """Build an orthogonal random forest from a training set (Y, T, X, W).
 
         Parameters

econml/tests/test_inference.py

@@ -44,8 +44,8 @@ def test_summary(self):
             cate_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             summary_results = cate_est.summary()
@@ -64,8 +64,8 @@ def test_summary(self):
             cate_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
@@ -78,8 +78,8 @@ def test_summary(self):
             cate_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             summary_results = cate_est.summary()
@@ -90,8 +90,8 @@ def test_summary(self):
             cate_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
@@ -104,8 +104,8 @@ def test_summary(self):
             wrapped_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             summary_results = wrapped_est.summary()
@@ -117,8 +117,8 @@ def test_summary(self):
             wrapped_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
@@ -138,8 +138,8 @@ def test_summary_discrete(self):
             cate_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             summary_results = cate_est.summary(T=1)
@@ -159,8 +159,8 @@ def test_summary_discrete(self):
             cate_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
@@ -174,8 +174,8 @@ def test_summary_discrete(self):
             cate_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             summary_results = cate_est.summary(T=1)
@@ -187,8 +187,8 @@ def test_summary_discrete(self):
             cate_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
@@ -202,8 +202,8 @@ def test_summary_discrete(self):
             wrapped_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             summary_results = wrapped_est.summary(T=1)
@@ -216,8 +216,8 @@ def test_summary_discrete(self):
             wrapped_est.fit(
                 TestInference.Y,
                 TestInference.T,
-                TestInference.X,
-                TestInference.W,
+                X=TestInference.X,
+                W=TestInference.W,
                 inference=inference
             )
             fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
@@ -275,16 +275,16 @@ def test_can_summarize(self):
         LinearDML(model_t=LinearRegression(), model_y=LinearRegression()).fit(
             TestInference.Y,
             TestInference.T,
-            TestInference.X,
-            TestInference.W
+            X=TestInference.X,
+            W=TestInference.W
         ).summary()
 
         LinearDRLearner(model_regression=LinearRegression(),
                         model_propensity=LogisticRegression(), fit_cate_intercept=False).fit(
             TestInference.Y,
             TestInference.T > 0,
-            TestInference.X,
-            TestInference.W,
+            X=TestInference.X,
+            W=TestInference.W,
             inference=BootstrapInference(5)
         ).summary(1)