Adjust ate inference to get the exact stderr when final stage is linear.

econml/dml/causal_forest.py

@@ -79,7 +79,7 @@ def const_marginal_effect_inference(self, X):
         pred = pred.reshape((-1,) + self._d_y + self._d_t)
         pred_stderr = np.sqrt(np.diagonal(pred_var, axis1=2, axis2=3).reshape((-1,) + self._d_y + self._d_t))
         return NormalInferenceResults(d_t=self.d_t, d_y=self.d_y, pred=pred,
-                                      pred_stderr=pred_stderr, inf_type='effect')
+                                      pred_stderr=pred_stderr, mean_pred_stderr=None, inf_type='effect')
 
     def effect_interval(self, X, *, T0, T1, alpha=0.1):
         return self.effect_inference(X, T0=T0, T1=T1).conf_int(alpha=alpha)
@@ -97,7 +97,7 @@ def effect_inference(self, X, *, T0, T1):
         pred = pred.reshape((-1,) + self._d_y)
         pred_stderr = np.sqrt(pred_var.reshape((-1,) + self._d_y))
         return NormalInferenceResults(d_t=None, d_y=self.d_y, pred=pred,
-                                      pred_stderr=pred_stderr, inf_type='effect')
+                                      pred_stderr=pred_stderr, mean_pred_stderr=None, inf_type='effect')
 
 
 class CausalForestDML(_BaseDML):

econml/inference/_bootstrap.py

@@ -264,7 +264,7 @@ def normal_inference(*args, **kwargs):
                         stderr = stderr(*args, **kwargs)
                     return NormalInferenceResults(
                         d_t=d_t, d_y=d_y, pred=pred,
-                        pred_stderr=stderr, inf_type=inf_type,
+                        pred_stderr=stderr, mean_pred_stderr=None, inf_type=inf_type,
                         fname_transformer=fname_transformer,
                         feature_names=self._wrapped.cate_feature_names(),
                         output_names=self._wrapped.cate_output_names(),

econml/inference/_inference.py

@@ -147,7 +147,7 @@ def const_marginal_effect_inference(self, X):
             warn("Final model doesn't have a `prediction_stderr` method, "
                  "only point estimates will be returned.")
         return NormalInferenceResults(d_t=self.d_t, d_y=self.d_y, pred=pred,
-                                      pred_stderr=pred_stderr, inf_type='effect',
+                                      pred_stderr=pred_stderr, mean_pred_stderr=None, inf_type='effect',
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names(),
                                       treatment_names=self._est.cate_treatment_names())
@@ -193,9 +193,10 @@ def effect_inference(self, X, *, T0, T1):
         e_pred = np.einsum(einsum_str, cme_pred, dT)
         e_stderr = np.einsum(einsum_str, cme_stderr, np.abs(dT)) if cme_stderr is not None else None
         d_y = self._d_y[0] if self._d_y else 1
+
         # d_t=None here since we measure the effect across all Ts
         return NormalInferenceResults(d_t=None, d_y=d_y, pred=e_pred,
-                                      pred_stderr=e_stderr, inf_type='effect',
+                                      pred_stderr=e_stderr, mean_pred_stderr=None, inf_type='effect',
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names())
 
@@ -240,15 +241,38 @@ def effect_inference(self, X, *, T0, T1):
             X = np.ones((T0.shape[0], 1))
         elif self.featurizer is not None:
             X = self.featurizer.transform(X)
-        e_pred = self._predict(cross_product(X, T1 - T0))
-        e_stderr = self._prediction_stderr(cross_product(X, T1 - T0))
+        XT = cross_product(X, T1 - T0)
+        e_pred = self._predict(XT)
+        e_stderr = self._prediction_stderr(XT)
         d_y = self._d_y[0] if self._d_y else 1
+
+        mean_XT = XT.mean(axis=0, keepdims=True)
+        mean_pred_stderr = self._prediction_stderr(mean_XT)  # shape[0] will always be 1 here
+        # squeeze the first axis
+        mean_pred_stderr = np.squeeze(mean_pred_stderr, axis=0) if mean_pred_stderr is not None else None
         # d_t=None here since we measure the effect across all Ts
         return NormalInferenceResults(d_t=None, d_y=d_y, pred=e_pred,
-                                      pred_stderr=e_stderr, inf_type='effect',
+                                      pred_stderr=e_stderr, mean_pred_stderr=mean_pred_stderr, inf_type='effect',
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names())
 
+    def const_marginal_effect_inference(self, X):
+        inf_res = super().const_marginal_effect_inference(X)
+
+        # set the mean_pred_stderr
+        if X is None:
+            X = np.ones((1, 1))
+        elif self.featurizer is not None:
+            X = self.featurizer.transform(X)
+        X_mean, T_mean = broadcast_unit_treatments(X.mean(axis=0).reshape(1, -1), self.d_t)
+        mean_XT = cross_product(X_mean, T_mean)
+        mean_pred_stderr = self._prediction_stderr(mean_XT)
+        if mean_pred_stderr is not None:
+            mean_pred_stderr = reshape_treatmentwise_effects(mean_pred_stderr,
+                                                             self._d_t, self._d_y)  # shape[0] will always be 1 here
+            inf_res.mean_pred_stderr = np.squeeze(mean_pred_stderr, axis=0)
+        return inf_res
+
     def coef__interval(self, *, alpha=0.1):
         lo, hi = self.model_final.coef__interval(alpha)
         lo_int, hi_int = self.model_final.intercept__interval(alpha)
@@ -285,6 +309,7 @@ def coef__inference(self):
             fname_transformer = self._est.cate_feature_names
 
         return NormalInferenceResults(d_t=self.d_t, d_y=self.d_y, pred=coef, pred_stderr=coef_stderr,
+                                      mean_pred_stderr=None,
                                       inf_type='coefficient', fname_transformer=fname_transformer,
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names(),
@@ -323,6 +348,7 @@ def intercept__inference(self):
             intercept_stderr = None
 
         return NormalInferenceResults(d_t=self.d_t, d_y=self.d_y, pred=intercept, pred_stderr=intercept_stderr,
+                                      mean_pred_stderr=None,
                                       inf_type='intercept',
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names(),
@@ -380,11 +406,7 @@ def fit(self, estimator, *args, **kwargs):
             self.fit_cate_intercept = estimator.fit_cate_intercept
 
     def const_marginal_effect_interval(self, X, *, alpha=0.1):
-        if (X is not None) and (self.featurizer is not None):
-            X = self.featurizer.transform(X)
-        preds = np.array([tuple(map(lambda x: x.reshape((-1,) + self._d_y), mdl.predict_interval(X, alpha=alpha)))
-                          for mdl in self.fitted_models_final])
-        return tuple(np.moveaxis(preds, [0, 1], [-1, 0]))  # send treatment to the end, pull bounds to the front
+        return self.const_marginal_effect_inference(X).conf_int(alpha=alpha)
 
     def const_marginal_effect_inference(self, X):
         if (X is not None) and (self.featurizer is not None):
@@ -401,22 +423,14 @@ def const_marginal_effect_inference(self, X):
                  "Only point estimates will be available.")
             pred_stderr = None
         return NormalInferenceResults(d_t=self.d_t, d_y=self.d_y, pred=pred,
-                                      pred_stderr=pred_stderr, inf_type='effect',
+                                      pred_stderr=pred_stderr, mean_pred_stderr=None,
+                                      inf_type='effect',
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names(),
                                       treatment_names=self._est.cate_treatment_names())
 
     def effect_interval(self, X, *, T0, T1, alpha=0.1):
-        X, T0, T1 = self._est._expand_treatments(X, T0, T1)
-        if np.any(np.any(T0 > 0, axis=1)):
-            raise AttributeError("Can only calculate intervals of effects with respect to baseline treatment!")
-        ind = inverse_onehot(T1)
-        lower, upper = self.const_marginal_effect_interval(X, alpha=alpha)
-        lower = np.concatenate([np.zeros(lower.shape[0:-1] + (1,)), lower], -1)
-        upper = np.concatenate([np.zeros(upper.shape[0:-1] + (1,)), upper], -1)
-        if X is None:  # Then const_marginal_effect_interval will return a single row
-            lower, upper = np.repeat(lower, T0.shape[0], axis=0), np.repeat(upper, T0.shape[0], axis=0)
-        return lower[np.arange(T0.shape[0]), ..., ind], upper[np.arange(T0.shape[0]), ..., ind]
+        return self.effect_inference(X, T0=T0, T1=T1).conf_int(alpha=alpha)
 
     def effect_inference(self, X, *, T0, T1):
         X, T0, T1 = self._est._expand_treatments(X, T0, T1)
@@ -434,9 +448,10 @@ def effect_inference(self, X, *, T0, T1):
             pred_stderr = np.repeat(pred_stderr, T0.shape[0], axis=0) if pred_stderr is not None else None
         pred = pred[np.arange(T0.shape[0]), ..., ind]
         pred_stderr = pred_stderr[np.arange(T0.shape[0]), ..., ind] if pred_stderr is not None else None
+
         # d_t=None here since we measure the effect across all Ts
         return NormalInferenceResults(d_t=None, d_y=self.d_y, pred=pred,
-                                      pred_stderr=pred_stderr,
+                                      pred_stderr=pred_stderr, mean_pred_stderr=None,
                                       inf_type='effect',
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names())
@@ -449,6 +464,21 @@ class LinearModelFinalInferenceDiscrete(GenericModelFinalInferenceDiscrete):
     based on the corresponding methods of the underlying model_final estimator.
     """
 
+    def const_marginal_effect_inference(self, X):
+        res_inf = super().const_marginal_effect_inference(X)
+
+        # set the mean_pred_stderr
+        if (X is not None) and (self.featurizer is not None):
+            X = self.featurizer.transform(X)
+
+        if hasattr(self.fitted_models_final[0], 'prediction_stderr'):
+            mean_X = X.mean(axis=0).reshape(1, -1) if X is not None else None
+            mean_pred_stderr = np.moveaxis(np.array([mdl.prediction_stderr(mean_X).reshape((-1,) + self._d_y)
+                                                     for mdl in self.fitted_models_final]),
+                                           0, -1)  # shape[0] will always be 1 here
+            res_inf.mean_pred_stderr = np.squeeze(mean_pred_stderr, axis=0)
+        return res_inf
+
     def coef__interval(self, T, *, alpha=0.1):
         _, T = self._est._expand_treatments(None, T)
         ind = inverse_onehot(T).item() - 1
@@ -472,8 +502,10 @@ def coef__inference(self, T):
         fname_transformer = None
         if hasattr(self._est, 'cate_feature_names') and callable(self._est.cate_feature_names):
             fname_transformer = self._est.cate_feature_names
+
         # d_t=None here since we measure the effect across all Ts
         return NormalInferenceResults(d_t=None, d_y=self.d_y, pred=coef, pred_stderr=coef_stderr,
+                                      mean_pred_stderr=None,
                                       inf_type='coefficient', fname_transformer=fname_transformer,
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names())
@@ -500,7 +532,7 @@ def intercept__inference(self, T):
             intercept_stderr = None
         # d_t=None here since we measure the effect across all Ts
         return NormalInferenceResults(d_t=None, d_y=self.d_y, pred=self.fitted_models_final[ind].intercept_,
-                                      pred_stderr=intercept_stderr,
+                                      pred_stderr=intercept_stderr, mean_pred_stderr=None,
                                       inf_type='intercept',
                                       feature_names=self._est.cate_feature_names(),
                                       output_names=self._est.cate_output_names())
@@ -748,7 +780,6 @@ def summary_frame(self, alpha=0.1, value=0, decimals=3,
 
         elif self.inf_type == 'intercept':
             res.index = res.index.set_levels(['cate_intercept'], level="X")
-
         if self._d_t == 1:
             res.index = res.index.droplevel("T")
         if self.d_y == 1:
@@ -786,6 +817,7 @@ def population_summary(self, alpha=0.1, value=0, decimals=3, tol=0.001, output_n
         output_names = self.output_names if output_names is None else output_names
         if self.inf_type == 'effect':
             return PopulationSummaryResults(pred=self.point_estimate, pred_stderr=self.stderr,
+                                            mean_pred_stderr=None,
                                             d_t=self.d_t, d_y=self.d_y,
                                             alpha=alpha, value=value, decimals=decimals, tol=tol,
                                             output_names=output_names, treatment_names=treatment_names)
@@ -839,16 +871,21 @@ class NormalInferenceResults(InferenceResults):
         Note that when Y or T is a vector rather than a 2-dimensional array,
         the corresponding singleton dimensions should be collapsed
         (e.g. if both are vectors, then the input of this argument will also be a vector)
+    mean_pred_stderr: None or array-like or scaler, shape (d_y, d_t) or (d_y,)
+        The standard error of the mean point estimate, this is derived from coefficient stderr when final
+        stage is linear model, otherwise it's None.
+        This is the exact standard error of the mean, which is not conservative.
     inf_type: string
         The type of inference result.
         It could be either 'effect', 'coefficient' or 'intercept'.
     fname_transformer: None or predefined function
         The transform function to get the corresponding feature names from featurizer
     """
 
-    def __init__(self, d_t, d_y, pred, pred_stderr, inf_type, fname_transformer=None,
+    def __init__(self, d_t, d_y, pred, pred_stderr, mean_pred_stderr, inf_type, fname_transformer=None,
                  feature_names=None, output_names=None, treatment_names=None):
         self.pred_stderr = pred_stderr
+        self.mean_pred_stderr = mean_pred_stderr
         super().__init__(d_t, d_y, pred, inf_type, fname_transformer, feature_names, output_names, treatment_names)
 
     @property
@@ -914,11 +951,20 @@ def pvalue(self, value=0):
         """
         return norm.sf(np.abs(self.zstat(value)), loc=0, scale=1) * 2
 
+    def population_summary(self, alpha=0.1, value=0, decimals=3, tol=0.001, output_names=None, treatment_names=None):
+        pop_summ = super().population_summary(alpha=alpha, value=value, decimals=decimals,
+                                              tol=tol, output_names=output_names, treatment_names=treatment_names)
+        pop_summ.mean_pred_stderr = self.mean_pred_stderr
+        return pop_summ
+    population_summary.__doc__ = InferenceResults.population_summary.__doc__
+
     def _expand_outputs(self, n_rows):
         assert shape(self.pred)[0] == shape(self.pred_stderr)[0] == 1
         pred = np.repeat(self.pred, n_rows, axis=0)
         pred_stderr = np.repeat(self.pred_stderr, n_rows, axis=0) if self.pred_stderr is not None else None
-        return NormalInferenceResults(self.d_t, self.d_y, pred, pred_stderr, self.inf_type,
+        return NormalInferenceResults(self.d_t, self.d_y, pred, pred_stderr,
+                                      self.mean_pred_stderr,
+                                      self.inf_type,
                                       self.fname_transformer, self.feature_names,
                                       self.output_names, self.treatment_names)
 
@@ -1038,6 +1084,10 @@ class PopulationSummaryResults:
         Note that when Y or T is a vector rather than a 2-dimensional array,
         the corresponding singleton dimensions should be collapsed
         (e.g. if both are vectors, then the input of this argument will also be a vector)
+    mean_pred_stderr: None or array-like or scaler, shape (d_y, d_t) or (d_y,)
+        The standard error of the mean point estimate, this is derived from coefficient stderr when final
+        stage is linear model, otherwise it's None.
+        This is the exact standard error of the mean, which is not conservative.
     alpha: optional float in [0, 1] (default=0.1)
         The overall level of confidence of the reported interval.
         The alpha/2, 1-alpha/2 confidence interval is reported.
@@ -1054,10 +1104,11 @@ class PopulationSummaryResults:
 
     """
 
-    def __init__(self, pred, pred_stderr, d_t, d_y, alpha, value, decimals, tol,
+    def __init__(self, pred, pred_stderr, mean_pred_stderr, d_t, d_y, alpha, value, decimals, tol,
                  output_names=None, treatment_names=None):
         self.pred = pred
         self.pred_stderr = pred_stderr
+        self.mean_pred_stderr = mean_pred_stderr
         self.d_t = d_t
         # For effect summaries, d_t is None, but the result arrays behave as if d_t=1
         self._d_t = d_t or 1
@@ -1105,7 +1156,9 @@ def stderr_mean(self):
             the corresponding singleton dimensions in the output will be collapsed
             (e.g. if both are vectors, then the output of this method will be a scalar)
         """
-        if self.pred_stderr is None:
+        if self.mean_pred_stderr is not None:
+            return self.mean_pred_stderr
+        elif self.pred_stderr is None:
             raise AttributeError("Only point estimates are available!")
         return np.sqrt(np.mean(self.pred_stderr**2, axis=0))
 
@@ -1311,13 +1364,13 @@ def _print(self, *, alpha=None, value=None, decimals=None, tol=None, output_name
                               self._format_res(self.pvalue(value=value), decimals),
                               self._format_res(self.conf_int_mean(alpha=alpha)[0], decimals),
                               self._format_res(self.conf_int_mean(alpha=alpha)[1], decimals)))
-
         if treatment_names is None:
             treatment_names = ['T' + str(i) for i in range(self._d_t)]
         if output_names is None:
             output_names = ['Y' + str(i) for i in range(self.d_y)]
 
         myheaders1 = ['mean_point', 'stderr_mean', 'zstat', 'pvalue', 'ci_mean_lower', 'ci_mean_upper']
+
         mystubs = self._get_stub_names(self.d_y, self._d_t, treatment_names, output_names)
         title1 = "Uncertainty of Mean Point Estimate"
 
@@ -1330,13 +1383,12 @@ def _print(self, *, alpha=None, value=None, decimals=None, tol=None, output_name
 
         smry = Summary()
         smry.add_table(res1, myheaders1, mystubs, title1)
-        if self.pred_stderr is not None:
+        if self.pred_stderr is not None and self.mean_pred_stderr is None:
             text1 = "Note: The stderr_mean is a conservative upper bound."
             smry.add_extra_txt([text1])
         smry.add_table(res2, myheaders2, mystubs, title2)
 
         if self.pred_stderr is not None:
-
             # 3. Total Variance of Point Estimate
             res3 = np.hstack((self._format_res(self.stderr_point, self.decimals),
                               self._format_res(self.conf_int_point(alpha=alpha, tol=tol)[0],