class_docstr: Class for the estimate object that every causal estimator returns
class_docstr_tok: ["Class","for","the","estimate","object","that","every","causal","estimator","returns"]
docstr: Interpret the causal estimate.
  
  :param method_name: Method used (string) or a list of methods. If None, then the default for the specific estimator is used.
  :param kwargs:: Optional parameters that are directly passed to the interpreter method.
  
  :returns: None
code: def interpret(self, method_name=None, **kwargs):
      """Interpret the causal estimate.
  
      :param method_name: Method used (string) or a list of methods. If None, then the default for the specific estimator is used.
      :param kwargs:: Optional parameters that are directly passed to the interpreter method.
  
      :returns: None
  
      """
      if method_name is None:
          method_name = self.estimator.interpret_method
      method_name_arr = parse_state(method_name)
  
      for method in method_name_arr:
          interpreter = interpreters.get_class_object(method)
          interpreter(self, **kwargs).interpret(self._data)
  
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 CausalEstimate obj's underlying member method code with example usage: obj.interpret(method_name) without return types