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.
class_docstr_tok: ["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","."]
docstr: Fits the estimator with data for effect estimation
  :param data: data frame containing the data
  :param treatment: name of the treatment variable
  :param outcome: name of the outcome variable
  :param effect_modifiers: Variables on which to compute separate
              effects, or return a heterogeneous effect function. Not all
              methods support this currently.
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code: def fit(
      self,
      data: pd.DataFrame,
      effect_modifier_names: Optional[List[str]] = None,
  ):
      """
      Fits the estimator with data for effect estimation
      :param data: data frame containing the data
      :param treatment: name of the treatment variable
      :param outcome: name of the outcome variable
      :param effect_modifiers: Variables on which to compute separate
                  effects, or return a heterogeneous effect function. Not all
                  methods support this currently.
      """
      return super().fit(
          data, effect_modifier_names=effect_modifier_names
      )
  
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 2 for calling the LinearRegressionEstimator obj's underlying member method code with example usage: obj.fit(data, effect_modifier_names) and returns: super