Vasilis/autoinference

econml/cate_estimator.py

@@ -486,6 +486,11 @@ class LinearModelFinalCateEstimatorMixin(BaseCateEstimator):
         than as a separate ``intercept_``
     """
 
+    def _get_inference_options(self):
+        options = super()._get_inference_options()
+        options.update(auto=LinearModelFinalInference)
+        return options
+
     bias_part_of_coef = False
 
     @property
@@ -609,6 +614,14 @@ def summary(self, alpha=0.1, value=0, decimals=3, feat_name=None):
             converted to various output formats.
         """
         smry = Summary()
+        smry.add_extra_txt(["<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:",
+                            "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$",
+                            "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:",
+                            "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$",
+                            "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. "
+                            "Coefficient Results table portrays the $coef_{ij}$ parameter vector for "
+                            "each outcome $i$ and treatment $j$. "
+                            "Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"])
         d_t = self._d_t[0] if self._d_t else 1
         d_y = self._d_y[0] if self._d_y else 1
         try:
@@ -630,10 +643,10 @@ def summary(self, alpha=0.1, value=0, decimals=3, feat_name=None):
                                  in intercept_table.columns] if d_t > 1 else intercept_table.columns.tolist()
             intercept_stubs = [i + ' | ' + j for (i, j)
                                in intercept_table.index] if d_y > 1 else intercept_table.index.tolist()
-            intercept_title = 'Intercept Results'
+            intercept_title = 'CATE Intercept Results'
             smry.add_table(intercept_array, intercept_headers, intercept_stubs, intercept_title)
         except Exception as e:
-            print("Intercept Results: ", str(e))
+            print("CATE Intercept Results: ", str(e))
         if len(smry.tables) > 0:
             return smry
 
@@ -652,6 +665,7 @@ def _get_inference_options(self):
         # add statsmodels to parent's options
         options = super()._get_inference_options()
         options.update(statsmodels=StatsModelsInference)
+        options.update(auto=StatsModelsInference)
         return options
 
 
@@ -662,6 +676,7 @@ def _get_inference_options(self):
         # add debiasedlasso to parent's options
         options = super()._get_inference_options()
         options.update(debiasedlasso=LinearModelFinalInference)
+        options.update(auto=LinearModelFinalInference)
         return options
 
 
@@ -671,6 +686,7 @@ def _get_inference_options(self):
         # add blb to parent's options
         options = super()._get_inference_options()
         options.update(blb=GenericSingleTreatmentModelFinalInference)
+        options.update(auto=GenericSingleTreatmentModelFinalInference)
         return options
 
     @property
@@ -687,6 +703,11 @@ class LinearModelFinalCateEstimatorDiscreteMixin(BaseCateEstimator):
     returning an array of the fitted models for each non-control treatment
     """
 
+    def _get_inference_options(self):
+        options = super()._get_inference_options()
+        options.update(auto=LinearModelFinalInferenceDiscrete)
+        return options
+
     def coef_(self, T):
         """ The coefficients in the linear model of the constant marginal treatment
         effect associated with treatment T.
@@ -826,6 +847,15 @@ def summary(self, T, *, alpha=0.1, value=0, decimals=3, feat_name=None):
             converted to various output formats.
         """
         smry = Summary()
+        smry.add_extra_txt(["<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:",
+                            "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$",
+                            "where $T$ is the one-hot-encoding of the discrete treatment and "
+                            "for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:",
+                            "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$",
+                            "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. "
+                            "Coefficient Results table portrays the $coef_{ij}$ parameter vector for "
+                            "each outcome $i$ and the designated treatment $j$ passed to summary. "
+                            "Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"])
         try:
             coef_table = self.coef__inference(T).summary_frame(
                 alpha=alpha, value=value, decimals=decimals, feat_name=feat_name)
@@ -842,10 +872,10 @@ def summary(self, T, *, alpha=0.1, value=0, decimals=3, feat_name=None):
             intercept_array = intercept_table.values
             intercept_headers = intercept_table.columns.tolist()
             intercept_stubs = intercept_table.index.tolist()
-            intercept_title = 'Intercept Results'
+            intercept_title = 'CATE Intercept Results'
             smry.add_table(intercept_array, intercept_headers, intercept_stubs, intercept_title)
         except Exception as e:
-            print("Intercept Results: ", e)
+            print("CATE Intercept Results: ", e)
 
         if len(smry.tables) > 0:
             return smry
@@ -866,6 +896,7 @@ def _get_inference_options(self):
         # add statsmodels to parent's options
         options = super()._get_inference_options()
         options.update(statsmodels=StatsModelsInferenceDiscrete)
+        options.update(auto=StatsModelsInferenceDiscrete)
         return options
 
 
@@ -876,6 +907,7 @@ def _get_inference_options(self):
         # add statsmodels to parent's options
         options = super()._get_inference_options()
         options.update(debiasedlasso=LinearModelFinalInferenceDiscrete)
+        options.update(auto=LinearModelFinalInferenceDiscrete)
         return options
 
 
@@ -885,6 +917,7 @@ def _get_inference_options(self):
         # add blb to parent's options
         options = super()._get_inference_options()
         options.update(blb=GenericModelFinalInferenceDiscrete)
+        options.update(auto=GenericModelFinalInferenceDiscrete)
         return options
 
     def feature_importances_(self, T):

econml/dml.py

@@ -447,6 +447,35 @@ def __init__(self,
                          n_splits=n_splits,
                          random_state=random_state)
 
+    # override only so that we can update the docstring to indicate support for `StatsModelsInference`
+    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference='auto'):
+        """
+        Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
+
+        Parameters
+        ----------
+        Y: (n × d_y) matrix or vector of length n
+            Outcomes for each sample
+        T: (n × dₜ) matrix or vector of length n
+            Treatments for each sample
+        X: optional (n × dₓ) matrix
+            Features for each sample
+        W: optional (n × d_w) matrix
+            Controls for each sample
+        sample_weight: optional (n,) vector
+            Weights for each row
+        inference: string, :class:`.Inference` instance, or None
+            Method for performing inference.  This estimator supports 'bootstrap'
+            (or an instance of :class:`.BootstrapInference`) and 'auto'
+            (or an instance of :class:`.LinearModelFinalInference`)
+
+        Returns
+        -------
+        self
+        """
+        return super().fit(Y, T, X=X, W=W, sample_weight=sample_weight, sample_var=sample_var, groups=groups,
+                           inference=inference)
+
 
 class LinearDMLCateEstimator(StatsModelsCateEstimatorMixin, DMLCateEstimator):
     """
@@ -529,7 +558,7 @@ def __init__(self,
                          random_state=random_state)
 
     # override only so that we can update the docstring to indicate support for `StatsModelsInference`
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference=None):
+    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
 
@@ -675,7 +704,7 @@ def __init__(self,
                          n_splits=n_splits,
                          random_state=random_state)
 
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference=None):
+    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
 
@@ -1076,7 +1105,7 @@ def __init__(self,
                          categories=categories,
                          n_splits=n_crossfit_splits, random_state=random_state)
 
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference=None):
+    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
 

econml/drlearner.py

@@ -688,7 +688,7 @@ def __init__(self,
                          n_splits=n_splits,
                          random_state=random_state)
 
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference=None):
+    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.
 
@@ -903,7 +903,7 @@ def __init__(self,
                          n_splits=n_splits,
                          random_state=random_state)
 
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference=None):
+    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.
 
@@ -1158,7 +1158,7 @@ def __init__(self,
                          categories=categories,
                          n_splits=n_crossfit_splits, random_state=random_state)
 
-    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference=None):
+    def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
 

econml/inference.py

@@ -598,7 +598,7 @@ def zstat(self, value=0):
             the corresponding singleton dimensions in the output will be collapsed
             (e.g. if both are vectors, then the output of this method will also be a vector)
         """
-        return (value - self.point_estimate) / self.stderr
+        return (self.point_estimate - value) / self.stderr
 
     def summary_frame(self, alpha=0.1, value=0, decimals=3, feat_name=None):
         """
@@ -651,9 +651,9 @@ def summary_frame(self, alpha=0.1, value=0, decimals=3, feat_name=None):
                 res.index = ind
         elif self.inf_type == 'intercept':
             if self.d_y > 1:
-                res.index = res.index.set_levels(['intercept'], level=0)
+                res.index = res.index.set_levels(['cate_intercept'], level=0)
             else:
-                res.index = ['intercept']
+                res.index = ['cate_intercept']
         return res
 
     def population_summary(self, alpha=0.1, value=0, decimals=3, tol=0.001):

econml/ortho_forest.py

@@ -265,6 +265,7 @@ def _get_inference_options(self):
         # Add blb inference to parent's options
         options = super()._get_inference_options()
         options.update(blb=BLBInference)
+        options.update(auto=BLBInference)
         return options
 
     def _pointwise_effect(self, X_single, stderr=False):
@@ -765,7 +766,7 @@ def __init__(self,
             n_jobs=n_jobs,
             random_state=random_state)
 
-    def fit(self, Y, T, X, W=None, inference=None):
+    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/ortho_iv.py

@@ -1428,7 +1428,7 @@ def __init__(self, model_Y_X, model_T_XZ,
                          categories=categories)
 
     # override only so that we can update the docstring to indicate support for `StatsModelsInference`
-    def fit(self, Y, T, Z, X=None, W=None, sample_weight=None, sample_var=None, groups=None, inference=None):
+    def fit(self, Y, T, Z, X=None, W=None, sample_weight=None, sample_var=None, groups=None, inference='auto'):
         """
         Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.