Mehei/otherinferences (#203)

* add analytical effect/marginal effect/constant marginal effect inferences for DML and DRLearner
* add coefficient inference and intercept inference for linear final model
* add population summary inference given dataset X

econml/_ortho_learner.py

@@ -543,11 +543,21 @@ def const_marginal_effect_interval(self, X=None, *, alpha=0.1):
         return super().const_marginal_effect_interval(X, alpha=alpha)
     const_marginal_effect_interval.__doc__ = LinearCateEstimator.const_marginal_effect_interval.__doc__
 
+    def const_marginal_effect_inference(self, X=None):
+        self._check_fitted_dims(X)
+        return super().const_marginal_effect_inference(X)
+    const_marginal_effect_inference.__doc__ = LinearCateEstimator.const_marginal_effect_inference.__doc__
+
     def effect_interval(self, X=None, *, T0=0, T1=1, alpha=0.1):
         self._check_fitted_dims(X)
         return super().effect_interval(X, T0=T0, T1=T1, alpha=alpha)
     effect_interval.__doc__ = LinearCateEstimator.effect_interval.__doc__
 
+    def effect_inference(self, X=None, *, T0=0, T1=1):
+        self._check_fitted_dims(X)
+        return super().effect_inference(X, T0=T0, T1=T1)
+    effect_inference.__doc__ = LinearCateEstimator.effect_inference.__doc__
+
     def score(self, Y, T, X=None, W=None, Z=None):
         """
         Score the fitted CATE model on a new data set. Generates nuisance parameters

econml/cate_estimator.py

@@ -12,7 +12,7 @@
 from .inference import BootstrapInference
 from .utilities import tensordot, ndim, reshape, shape, parse_final_model_params, inverse_onehot
 from .inference import StatsModelsInference, StatsModelsInferenceDiscrete, LinearModelFinalInference,\
-    LinearModelFinalInferenceDiscrete
+    LinearModelFinalInferenceDiscrete, InferenceResults
 
 
 class BaseCateEstimator(metaclass=abc.ABCMeta):
@@ -197,6 +197,30 @@ def effect_interval(self, X=None, *, T0=0, T1=1, alpha=0.1):
         """
         pass
 
+    @_defer_to_inference
+    def effect_inference(self, X=None, *, T0=0, T1=1):
+        """ Inference results for the quantities :math:`\\tau(X, T0, T1)` produced
+        by the model. Available only when ``inference`` is not ``None``, when
+        calling the fit method.
+
+        Parameters
+        ----------
+        X: optional (m, d_x) matrix
+            Features for each sample
+        T0: optional (m, d_t) matrix or vector of length m (Default=0)
+            Base treatments for each sample
+        T1: optional (m, d_t) matrix or vector of length m (Default=1)
+            Target treatments for each sample
+
+        Returns
+        -------
+        InferenceResults: object
+            The inference results instance contains prediction and prediction standard error and
+            can on demand calculate confidence interval, z statistic and p value. It can also output
+            a dataframe summary of these inference results.
+        """
+        pass
+
     @_defer_to_inference
     def marginal_effect_interval(self, T, X=None, *, alpha=0.1):
         """ Confidence intervals for the quantities :math:`\\partial \\tau(T, X)` produced
@@ -221,6 +245,28 @@ def marginal_effect_interval(self, T, X=None, *, alpha=0.1):
         """
         pass
 
+    @_defer_to_inference
+    def marginal_effect_inference(self, T, X=None):
+        """ Inference results for the quantities :math:`\\partial \\tau(T, X)` produced
+        by the model. Available only when ``inference`` is not ``None``, when
+        calling the fit method.
+
+        Parameters
+        ----------
+        T: (m, d_t) matrix
+            Base treatments for each sample
+        X: optional (m, d_x) matrix or None (Default=None)
+            Features for each sample
+
+        Returns
+        -------
+        InferenceResults: object
+            The inference results instance contains prediction and prediction standard error and
+            can on demand calculate confidence interval, z statistic and p value. It can also output
+            a dataframe summary of these inference results.
+        """
+        pass
+
 
 class LinearCateEstimator(BaseCateEstimator):
     """Base class for all CATE estimators with linear treatment effects in this package."""
@@ -324,6 +370,18 @@ def marginal_effect_interval(self, T, X=None, *, alpha=0.1):
                      for eff in effs)
     marginal_effect_interval.__doc__ = BaseCateEstimator.marginal_effect_interval.__doc__
 
+    def marginal_effect_inference(self, T, X=None):
+        X, T = self._expand_treatments(X, T)
+        cme_inf = self.const_marginal_effect_inference(X=X)
+        pred = cme_inf.point_estimate
+        pred_stderr = cme_inf.stderr
+        if X is None:
+            pred = np.repeat(pred, shape(T)[0], axis=0)
+            pred_stderr = np.repeat(pred_stderr, shape(T)[0], axis=0)
+        return InferenceResults(d_t=cme_inf.d_t, d_y=cme_inf.d_y, pred=pred,
+                                pred_stderr=pred_stderr, inf_type='effect', pred_dist=None, fname_transformer=None)
+    marginal_effect_inference.__doc__ = BaseCateEstimator.marginal_effect_inference.__doc__
+
     @BaseCateEstimator._defer_to_inference
     def const_marginal_effect_interval(self, X=None, *, alpha=0.1):
         """ Confidence intervals for the quantities :math:`\\theta(X)` produced
@@ -346,6 +404,26 @@ def const_marginal_effect_interval(self, X=None, *, alpha=0.1):
         """
         pass
 
+    @BaseCateEstimator._defer_to_inference
+    def const_marginal_effect_inference(self, X=None):
+        """ Inference results for the quantities :math:`\\theta(X)` produced
+        by the model. Available only when ``inference`` is not ``None``, when
+        calling the fit method.
+
+        Parameters
+        ----------
+        X: optional (m, d_x) matrix or None (Default=None)
+            Features for each sample
+
+        Returns
+        -------
+        InferenceResults: object
+            The inference results instance contains prediction and prediction standard error and
+            can on demand calculate confidence interval, z statistic and p value. It can also output
+            a dataframe summary of these inference results.
+        """
+        pass
+
 
 class TreatmentExpansionMixin(BaseCateEstimator):
     """Mixin which automatically handles promotions of scalar treatments to the appropriate shape."""
@@ -454,6 +532,18 @@ def coef__interval(self, *, alpha=0.1):
         """
         pass
 
+    @BaseCateEstimator._defer_to_inference
+    def coef__inference(self):
+        """ The inference of coefficients in the linear model of the constant marginal treatment
+        effect.
+
+        Returns
+        -------
+        InferenceResults: object
+            The inference of the coefficients in the final linear model
+        """
+        pass
+
     @BaseCateEstimator._defer_to_inference
     def intercept__interval(self, *, alpha=0.1):
         """ The intercept in the linear model of the constant marginal treatment
@@ -472,6 +562,43 @@ def intercept__interval(self, *, alpha=0.1):
         """
         pass
 
+    @BaseCateEstimator._defer_to_inference
+    def intercept__inference(self):
+        """ The inference of intercept in the linear model of the constant marginal treatment
+        effect.
+
+        Returns
+        -------
+        InferenceResults: object
+            The inference of the intercept in the final linear model
+        """
+        pass
+
+    @BaseCateEstimator._defer_to_inference
+    def summary(self, alpha=0.1, value=0, decimals=3, feat_name=None):
+        """ The summary of coefficient and intercept in the linear model of the constant marginal treatment
+        effect.
+
+        Parameters
+        ----------
+        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.
+        value: optinal float (default=0)
+            The mean value of the metric you'd like to test under null hypothesis.
+        decimals: optinal int (default=3)
+            Number of decimal places to round each column to.
+        feat_name: optional list of strings or None (default is None)
+            The input of the feature names
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be printed or
+            converted to various output formats.
+        """
+        pass
+
 
 class StatsModelsCateEstimatorMixin(LinearModelFinalCateEstimatorMixin):
     """
@@ -568,6 +695,23 @@ def coef__interval(self, T, *, alpha=0.1):
         """
         pass
 
+    @BaseCateEstimator._defer_to_inference
+    def coef__inference(self, T):
+        """ The inference for the coefficients in the linear model of the
+        constant marginal treatment effect associated with treatment T.
+
+        Parameters
+        ----------
+        T: alphanumeric
+            The input treatment for which we want the coefficients.
+
+        Returns
+        -------
+        InferenceResults: object
+            The inference of the coefficients in the final linear model
+        """
+        pass
+
     @BaseCateEstimator._defer_to_inference
     def intercept__interval(self, T, *, alpha=0.1):
         """ The intercept in the linear model of the constant marginal treatment
@@ -588,6 +732,49 @@ def intercept__interval(self, T, *, alpha=0.1):
         """
         pass
 
+    @BaseCateEstimator._defer_to_inference
+    def intercept__inference(self, T):
+        """ The inference of the intercept in the linear model of the constant marginal treatment
+        effect associated with treatment T.
+
+        Parameters
+        ----------
+        T: alphanumeric
+            The input treatment for which we want the coefficients.
+
+        Returns
+        -------
+        InferenceResults: object
+            The inference of the intercept in the final linear model
+
+        """
+        pass
+
+    @BaseCateEstimator._defer_to_inference
+    def summary(self, T, *, alpha=0.1, value=0, decimals=3, feat_name=None):
+        """ The summary of coefficient and intercept in the linear model of the constant marginal treatment
+        effect associated with treatment T.
+
+        Parameters
+        ----------
+        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.
+        value: optinal float (default=0)
+            The mean value of the metric you'd like to test under null hypothesis.
+        decimals: optinal int (default=3)
+            Number of decimal places to round each column to.
+        feat_name: optional list of strings or None (default is None)
+            The input of the feature names
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be printed or
+            converted to various output formats.
+        """
+        pass
+
 
 class StatsModelsCateEstimatorDiscreteMixin(LinearModelFinalCateEstimatorDiscreteMixin):
     """

econml/drlearner.py

@@ -638,6 +638,7 @@ def __init__(self,
                  fit_cate_intercept=True,
                  min_propensity=1e-6,
                  n_splits=2, random_state=None):
+        self.fit_cate_intercept = fit_cate_intercept
         super().__init__(model_propensity=model_propensity,
                          model_regression=model_regression,
                          model_final=StatsModelsLinearRegression(fit_intercept=fit_cate_intercept),
@@ -837,6 +838,7 @@ def __init__(self,
                  tol=1e-4,
                  min_propensity=1e-6,
                  n_splits=2, random_state=None):
+        self.fit_cate_intercept = fit_cate_intercept
         model_final = DebiasedLasso(
             alpha=alpha,
             fit_intercept=fit_cate_intercept,