class_docstr: Base class for an estimator of causal effect.
  
  Subclasses implement different estimation methods. All estimation methods are in the package "dowhy.causal_estimators"
class_docstr_tok: ["Base","class","for","an","estimator","of","causal","effect",".","Subclasses","implement","different","estimation","methods",".","All","estimation","methods","are","in","the","package","``","dowhy.causal_estimators","''"]
docstr: Method that implements the do-operator.
  
  Given a value x for the treatment, returns the expected value of the outcome when the treatment is intervened to a value x.
  
  :param x: Value of the treatment
  :param data_df: Data on which the do-operator is to be applied.
  
  :returns: Value of the outcome when treatment is intervened/set to x.
code: def do(self, x, data_df=None):
      """Method that implements the do-operator.
  
      Given a value x for the treatment, returns the expected value of the outcome when the treatment is intervened to a value x.
  
      :param x: Value of the treatment
      :param data_df: Data on which the do-operator is to be applied.
  
      :returns: Value of the outcome when treatment is intervened/set to x.
  
      """
      est = self._do(x, data_df)
      return est
  
imports: import copy
  import logging
  from collections import namedtuple
  from typing import Dict, List, Optional, Union
  import numpy
  import pandas
  import sympy
  from sklearn.utils import resample
  import dowhy.interpreters
  from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 9 for calling the CausalEstimator obj's underlying member method code with example usage: obj.do(x, data_df) and returns: est