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: Compute standard error using the bootstrap method. Standard error
  and confidence intervals use the same parameter num_simulations for
  the number of bootstrap simulations.
  
  :param num_simulations: Number of bootstrapped samples.
  :param sample_size_fraction: Fraction of data to be resampled.
  :returns: Standard error of the obtained estimate.
code: def _estimate_std_error_with_bootstrap(
      self,
      data: pd.DataFrame,
      num_simulations=None,
      sample_size_fraction=None,
  ):
      """Compute standard error using the bootstrap method. Standard error
      and confidence intervals use the same parameter num_simulations for
      the number of bootstrap simulations.
  
      :param num_simulations: Number of bootstrapped samples.
      :param sample_size_fraction: Fraction of data to be resampled.
      :returns: Standard error of the obtained estimate.
      """
      # Use existing params, if new user defined params are not present
      if num_simulations is None:
          num_simulations = self.num_simulations
      if sample_size_fraction is None:
          sample_size_fraction = self.sample_size_fraction
      # Checking if bootstrap_estimates are already computed
      if self._bootstrap_estimates is None:
          self._bootstrap_estimates = (
              self._generate_bootstrap_estimates(
                  data, num_simulations, sample_size_fraction
              )
          )
      elif CausalEstimator.is_bootstrap_parameter_changed(
          self._bootstrap_estimates.params, locals()
      ):
          # Check if any parameter is changed from the previous std error estimate
          self._bootstrap_estimates = (
              self._generate_bootstrap_estimates(
                  data, num_simulations, sample_size_fraction
              )
          )
  
      std_error = np.std(self._bootstrap_estimates.estimates)
      return std_error
  
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 15 for calling the CausalEstimator obj's underlying member method code with example usage: obj._estimate_std_error_with_bootstrap(data, num_simulations, sample_size_fraction) and returns: std_error