Mehei/otherinferences

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, fn_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 +562,18 @@ 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
+
 
 class StatsModelsCateEstimatorMixin(LinearModelFinalCateEstimatorMixin):
  """
@@ -568,6 +670,23 @@ 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 +707,24 @@ 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
+
 
 class StatsModelsCateEstimatorDiscreteMixin(LinearModelFinalCateEstimatorDiscreteMixin):
  """