class_docstr: Wrapper class for estimators from the EconML library.
  
  Supports additional parameters as listed below. For init and fit
  parameters of each estimator, refer to the EconML docs.
docstr: Inference (uncertainty) results produced by the underlying EconML estimator
  :param df: Features of the units to evaluate
  :param args: passed through to the underlying estimator
  :param kwargs: passed through to the underlying estimator
docstr_tok: ["Inference","(","uncertainty",")","results","produced","by","the","underlying","EconML","estimator",":","param","df",":","Features","of","the","units","to","evaluate",":","param","args",":","passed","through","to","the","underlying","estimator",":","param","kwargs",":","passed","through","to","the","underlying","estimator"]
code: def effect_inference(self, df: pd.DataFrame, *args, **kwargs):
      """
      Inference (uncertainty) results produced by the underlying EconML estimator
      :param df: Features of the units to evaluate
      :param args: passed through to the underlying estimator
      :param kwargs: passed through to the underlying estimator
      """
  
      def effect_inference_fun(filtered_df, T0, T1, *args, **kwargs):
          return self.estimator.effect_inference(
              filtered_df, T0=T0, T1=T1, *args, **kwargs
          )
  
      Xdf = df[self._effect_modifier_names] if df is not None else df
      return self.apply_multitreatment(
          Xdf, effect_inference_fun, *args, **kwargs
      )
  
imports: import inspect
  from importlib import import_module
  from typing import Any, Callable, List, Optional, Protocol, Union
  from warnings import warn
  import numpy
  import pandas
  from numpy.distutils.misc_util import is_sequence
  from dowhy.causal_estimator import CausalEstimate, CausalEstimator
  from dowhy.causal_identifier import IdentifiedEstimand
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 12 for calling the Econml obj's underlying member method code with example usage: obj.effect_inference(df) and returns: self, self