class_docstr: Base class for a sampler from the interventional distribution.
class_docstr_tok: ["Base","class","for","a","sampler","from","the","interventional","distribution","."]
docstr: This is more likely the implementation you'd like to use, but some methods may require overriding this method
  to make the treatment effective.
  :param x:
  :return:
docstr_tok: ["This","is","more","likely","the","implementation","you","'d","like","to","use",",","but","some","methods","may","require","overriding","this","method","to","make","the","treatment","effective",".",":","param","x",":",":","return",":"]
code: def make_treatment_effective(self, x):
      """
      This is more likely the implementation you'd like to use, but some methods may require overriding this method
      to make the treatment effective.
      :param x:
      :return:
      """
      if not self.keep_original_treatment:
          self._df[self._treatment_names] = x
  
file_path: dowhy/dowhy/do_sampler.py
imports: import logging
  from typing import List
  import networkx
  import numpy
  import pandas
  from dowhy import EstimandType, identify_effect_auto
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 4 for calling the DoSampler obj's underlying member method code with example usage: obj.make_treatment_effective(x) without return types