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: Effect of the actual treatment that was applied to each unit
  ("effect of Treatment on the Treated")
  :param df: Features of the units to evaluate
  :param args: passed through to estimator.effect()
  :param kwargs: passed through to estimator.effect()
docstr_tok: ["Effect","of","the","actual","treatment","that","was","applied","to","each","unit","(","``","effect","of","Treatment","on","the","Treated","''",")",":","param","df",":","Features","of","the","units","to","evaluate",":","param","args",":","passed","through","to","estimator.effect","(",")",":","param","kwargs",":","passed","through","to","estimator.effect","(",")"]
code: def effect_tt(
      self, df: pd.DataFrame, treatment_value, *args, **kwargs
  ):
      """
      Effect of the actual treatment that was applied to each unit
      ("effect of Treatment on the Treated")
      :param df: Features of the units to evaluate
      :param args: passed through to estimator.effect()
      :param kwargs: passed through to estimator.effect()
      """
  
      eff = self.effect(
          df[self._effect_modifier_names], *args, **kwargs
      ).reshape((len(df), len(treatment_value)))
  
      out = np.zeros(len(df))
      treatment_value = parse_state(treatment_value)
  
      eff = np.reshape(eff, (len(df), len(treatment_value)))
  
      # For each unit, return the estimated effect of the treatment value
      # that was actually applied to the unit
      for c, col in enumerate(treatment_value):
          out[
              df[self._target_estimand.treatment_variable[0]] == col
          ] = eff[
              df[self._target_estimand.treatment_variable[0]] == col, c
          ]
      return pd.Series(data=out, index=df.index)
  
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 13 for calling the Econml obj's underlying member method code with example usage: obj.effect_tt(df, treatment_value) and returns: pd