class_docstr: Compute effect of treatment using linear regression.
  
  Fits a regression model for estimating the outcome using treatment(s) and confounders. For a univariate treatment, the treatment effect is equivalent to the coefficient of the treatment variable.
  
  Simple method to show the implementation of a causal inference method that can handle multiple treatments and heterogeneity in treatment. Requires a strong assumption that all relationships from (T, W) to Y are linear.
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code: def _estimate_std_error(self, method=None):
      if self._effect_modifier_names:
          raise NotImplementedError
      else:
          std_error = self.model.bse[
              1 : (len(self._target_estimand.treatment_variable) + 1)
          ]
  
          # For a linear regression model, the causal effect of a variable is equal to the coefficient corresponding to the
          # variable. Hence, the model by default outputs the standard error corresponding to treatment=1 and control=0.
          # So for custom treatment and control values, we must multiply the standard error by the difference of the two.
          return (
              self._treatment_value - self._control_value
          ) * std_error.to_numpy()
  
file_path: dowhy/dowhy/causal_estimators/linear_regression_estimator.py
imports: import itertools
  from typing import List, Optional, Union
  import pandas
  import statsmodels.api
  from dowhy.causal_estimator import CausalEstimator
  from dowhy.causal_estimators.regression_estimator import RegressionEstimator
  from dowhy.causal_identifier import IdentifiedEstimand
label_desc: Use image node_id 7 for calling the LinearRegressionEstimator obj's underlying member method code with example usage: obj._estimate_std_error(method) and returns: unknown