Deprecate n_splits with cv

doc/spec/estimation/dml.rst

@@ -551,10 +551,10 @@ Usage FAQs
     If one uses cross-validated estimators as first stages, then model selection for the first stage models
     is performed automatically.
 
-- **How should I set the parameter `n_splits`?**
+- **How should I set the parameter `cv`?**
 
     This parameter defines the number of data partitions to create in order to fit the first stages in a
-    crossfittin manner (see :class:`._OrthoLearner`). The default is 2, which
+    crossfitting manner (see :class:`._OrthoLearner`). The default is 2, which
     is the minimal. However, larger values like 5 or 6 can lead to greater statistical stability of the method,
     especially if the number of samples is small. So we advise that for small datasets, one should raise this
     value. This can increase the computational cost as more first stage models are being fitted.

doc/spec/estimation/dr.rst

@@ -242,7 +242,7 @@ Below we give a brief description of each of these classes:
                             }, cv=10, n_jobs=-1, scoring='neg_mean_squared_error'
                         )
         est = DRLearner(model_regression=model_reg(), model_propensity=model_clf(),
-                        model_final=model_reg(), n_splits=5)
+                        model_final=model_reg(), cv=5)
         est.fit(y, T, X=X, W=W)
         point = est.effect(X, T0=T0, T1=T1)
 
@@ -427,7 +427,7 @@ Usage FAQs
                             }, cv=5, n_jobs=-1, scoring='neg_mean_squared_error'
                         )
         est = DRLearner(model_regression=model_reg(), model_propensity=model_clf(),
-                        model_final=model_reg(), n_splits=5)
+                        model_final=model_reg(), cv=5)
         est.fit(y, T, X=X, W=W)
         point = est.effect(X, T0=T0, T1=T1)
 
@@ -467,10 +467,10 @@ Usage FAQs
     If one uses cross-validated estimators as first stages, then model selection for the first stage models
     is performed automatically.
 
-- **How should I set the parameter `n_splits`?**
+- **How should I set the parameter `cv`?**
 
     This parameter defines the number of data partitions to create in order to fit the first stages in a
-    crossfittin manner (see :class:`._OrthoLearner`). The default is 2, which
+    crossfitting manner (see :class:`._OrthoLearner`). The default is 2, which
     is the minimal. However, larger values like 5 or 6 can lead to greater statistical stability of the method,
     especially if the number of samples is small. So we advise that for small datasets, one should raise this
     value. This can increase the computational cost as more first stage models are being fitted.

econml/_ortho_learner.py

@@ -259,7 +259,7 @@ class _OrthoLearner(TreatmentExpansionMixin, LinearCateEstimator):
         The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values).
         The first category will be treated as the control treatment.
 
-    n_splits: int, cross-validation generator or an iterable
+    cv: int, cross-validation generator or an iterable
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
 
@@ -333,7 +333,7 @@ def _gen_ortho_learner_model_final(self):
         np.random.seed(123)
         X = np.random.normal(size=(100, 3))
         y = X[:, 0] + X[:, 1] + np.random.normal(0, 0.1, size=(100,))
-        est = OrthoLearner(n_splits=2, discrete_treatment=False, discrete_instrument=False,
+        est = OrthoLearner(cv=2, discrete_treatment=False, discrete_instrument=False,
                            categories='auto', random_state=None)
         est.fit(y, X[:, 0], W=X[:, 1:])
 
@@ -391,7 +391,7 @@ def _gen_ortho_learner_model_final(self):
         import scipy.special
         T = np.random.binomial(1, scipy.special.expit(W[:, 0]))
         y = T + W[:, 0] + np.random.normal(0, 0.01, size=(100,))
-        est = OrthoLearner(n_splits=2, discrete_treatment=True, discrete_instrument=False,
+        est = OrthoLearner(cv=2, discrete_treatment=True, discrete_instrument=False,
                            categories='auto', random_state=None)
         est.fit(y, T, W=W)
 
@@ -424,8 +424,9 @@ def _gen_ortho_learner_model_final(self):
     """
 
     def __init__(self, *,
-                 discrete_treatment, discrete_instrument, categories, n_splits, random_state,
-                 mc_iters=None, mc_agg='mean'):
+                 discrete_treatment, discrete_instrument, categories, cv, random_state,
+                 n_splits='raise', mc_iters=None, mc_agg='mean'):
+        self.cv = cv
         self.n_splits = n_splits
         self.discrete_treatment = discrete_treatment
         self.discrete_instrument = discrete_instrument
@@ -566,7 +567,7 @@ def fit(self, Y, T, X=None, W=None, Z=None, *, sample_weight=None, sample_var=No
             Sample variance for each sample
         groups: (n,) vector, optional
             All rows corresponding to the same group will be kept together during splitting.
-            If groups is not None, the n_splits argument passed to this class's initializer
+            If groups is not None, the cv argument passed to this class's initializer
             must support a 'groups' argument to its split method.
         cache_values: bool, default False
             Whether to cache the inputs and computed nuisances, which will allow refitting a different final model
@@ -712,16 +713,16 @@ def _fit_nuisances(self, Y, T, X=None, W=None, Z=None, sample_weight=None, group
         if self.discrete_instrument:
             Z = self.z_transformer.transform(reshape(Z, (-1, 1)))
 
-        if self.n_splits == 1:  # special case, no cross validation
+        if self.cv == 1:  # special case, no cross validation
             folds = None
         else:
-            splitter = check_cv(self.n_splits, [0], classifier=stratify)
+            splitter = check_cv(self.cv, [0], classifier=stratify)
             # if check_cv produced a new KFold or StratifiedKFold object, we need to set shuffle and random_state
             # TODO: ideally, we'd also infer whether we need a GroupKFold (if groups are passed)
             #       however, sklearn doesn't support both stratifying and grouping (see
             #       https://github.com/scikit-learn/scikit-learn/issues/13621), so for now the user needs to supply
             #       their own object that supports grouping if they want to use groups.
-            if splitter != self.n_splits and isinstance(splitter, (KFold, StratifiedKFold)):
+            if splitter != self.cv and isinstance(splitter, (KFold, StratifiedKFold)):
                 splitter.shuffle = True
                 splitter.random_state = self._random_state
 
@@ -856,3 +857,18 @@ def models_nuisance_(self):
         if not hasattr(self, '_models_nuisance'):
             raise AttributeError("Model is not fitted!")
         return self._models_nuisance
+
+    #######################################################
+    # These should be removed once `n_splits` is deprecated
+    #######################################################
+
+    @property
+    def n_splits(self):
+        return self.cv
+
+    @n_splits.setter
+    def n_splits(self, value):
+        if value != 'raise':
+            warn("Parameter `n_splits` has been deprecated and will be removed in the next version. "
+                 "Use parameter `cv` instead.")
+            self.cv = value

econml/dml/_rlearner.py

@@ -147,7 +147,7 @@ class _RLearner(_OrthoLearner):
         The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values).
         The first category will be treated as the control treatment.
 
-    n_splits: int, cross-validation generator or an iterable
+    cv: int, cross-validation generator or an iterable
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
 
@@ -216,7 +216,7 @@ def _gen_rlearner_model_final(self):
         np.random.seed(123)
         X = np.random.normal(size=(1000, 3))
         y = X[:, 0] + X[:, 1] + np.random.normal(0, 0.01, size=(1000,))
-        est = RLearner(n_splits=2, discrete_treatment=False, categories='auto', random_state=None)
+        est = RLearner(cv=2, discrete_treatment=False, categories='auto', random_state=None)
         est.fit(y, X[:, 0], X=np.ones((X.shape[0], 1)), W=X[:, 1:])
 
     >>> est.const_marginal_effect(np.ones((1,1)))
@@ -261,10 +261,12 @@ def _gen_rlearner_model_final(self):
         is multidimensional, then the average of the MSEs for each dimension of Y is returned.
     """
 
-    def __init__(self, *, discrete_treatment, categories, n_splits, random_state, mc_iters=None, mc_agg='mean'):
+    def __init__(self, *, discrete_treatment, categories, cv, random_state,
+                 n_splits='raise', mc_iters=None, mc_agg='mean'):
         super().__init__(discrete_treatment=discrete_treatment,
                          discrete_instrument=False,  # no instrument, so doesn't matter
                          categories=categories,
+                         cv=cv,
                          n_splits=n_splits,
                          random_state=random_state,
                          mc_iters=mc_iters,
@@ -345,7 +347,7 @@ def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, grou
             Sample variance for each sample
         groups: (n,) vector, optional
             All rows corresponding to the same group will be kept together during splitting.
-            If groups is not None, the n_splits argument passed to this class's initializer
+            If groups is not None, the `cv` argument passed to this class's initializer
             must support a 'groups' argument to its split method.
         cache_values: bool, default False
             Whether to cache inputs and first stage results, which will allow refitting a different final model

econml/dml/causal_forest.py

@@ -439,8 +439,8 @@ def __init__(self, *,
             cv = self.n_crossfit_splits
         super().__init__(discrete_treatment=discrete_treatment,
                          categories=categories,
-                         # TODO. change to `cv=cv, n_splits='raise` when merged with the `n_splits` deprecation PR
-                         n_splits=cv,
+                         cv=cv,
+                         n_splits=n_crossfit_splits,
                          mc_iters=mc_iters,
                          mc_agg=mc_agg,
                          random_state=random_state)