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: Test statistical significance of the estimate obtained.
  
  By default, uses resampling to create a non-parametric significance test.
  Individual child estimators can implement different methods.
  If the method name is different from "bootstrap", this function calls the
  implementation of the child estimator.
  
  :param method: Method for checking statistical significance
  :param kwargs: Other optional parameters to be passed to the estimating method.
  
  :returns: p-value from the significance test
code: def test_stat_significance(self, method=None, **kwargs):
      """Test statistical significance of the estimate obtained.
  
      By default, uses resampling to create a non-parametric significance test.
      Individual child estimators can implement different methods.
      If the method name is different from "bootstrap", this function calls the
      implementation of the child estimator.
  
      :param method: Method for checking statistical significance
      :param kwargs: Other optional parameters to be passed to the estimating method.
  
      :returns: p-value from the significance test
      """
      signif_results = self.estimator.test_significance(
          self._data, self.value, method=method, **kwargs
      )
      return {"p_value": signif_results["p_value"]}
  
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 7 for calling the CausalEstimate obj's underlying member method code with example usage: obj.test_stat_significance(method) and returns: dict