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: Pointwise estimated treatment effect,
  output shape n_units x n_treatment_values (not counting control)
  :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: ["Pointwise","estimated","treatment","effect",",","output","shape","n_units","x","n_treatment_values","(","not","counting","control",")",":","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(self, df: pd.DataFrame, *args, **kwargs) -> np.ndarray:
      """
      Pointwise estimated treatment effect,
      output shape n_units x n_treatment_values (not counting control)
      :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_fun(filtered_df, T0, T1, *args, **kwargs):
          return self.estimator.effect(
              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_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 10 for calling the Econml obj's underlying member method code with example usage: obj.effect(df) and returns: self, self