class_docstr: Compute effect of treatment using some regression function.
  
  Fits a regression model for estimating the outcome using treatment(s) and
  confounders.
  
  Base class for all regression models, inherited by
  LinearRegressionEstimator and GeneralizedLinearModelEstimator.
class_docstr_tok: ["Compute","effect","of","treatment","using","some","regression","function",".","Fits","a","regression","model","for","estimating","the","outcome","using","treatment","(","s",")","and","confounders",".","Base","class","for","all","regression","models",",","inherited","by","LinearRegressionEstimator","and","GeneralizedLinearModelEstimator","."]
docstr: Removes any reference to data encoders, causing them to be re-created on next `fit()`.
  
  It's important that data is consistently encoded otherwise models will produce inconsistent output.
  In particular, categorical variables are one-hot encoded; the mapping of original data values
  must be identical between model training/fitting and inference time.
  
  Encoders are reset when `fit()` is called again, as the data is assumed to have changed.
  
  A separate encoder is used for each subset of variables (treatment, common causes and effect modifiers).
docstr_tok: ["Removes","any","reference","to","data","encoders",",","causing","them","to","be","re-created","on","next","`","fit","(",")","`",".","It","'s","important","that","data","is","consistently","encoded","otherwise","models","will","produce","inconsistent","output",".","In","particular",",","categorical","variables","are","one-hot","encoded",";","the","mapping","of","original","data","values","must","be","identical","between","model","training\/fitting","and","inference","time",".","Encoders","are","reset","when","`","fit","(",")","`","is","called","again",",","as","the","data","is","assumed","to","have","changed",".","A","separate","encoder","is","used","for","each","subset","of","variables","(","treatment",",","common","causes","and","effect","modifiers",")","."]
code: def reset_encoders(self):
      """
      Removes any reference to data encoders, causing them to be re-created on next `fit()`.
  
      It's important that data is consistently encoded otherwise models will produce inconsistent output.
      In particular, categorical variables are one-hot encoded; the mapping of original data values
      must be identical between model training/fitting and inference time.
  
      Encoders are reset when `fit()` is called again, as the data is assumed to have changed.
  
      A separate encoder is used for each subset of variables (treatment, common causes and effect modifiers).
      """
      self._encoders = {
          "treatment": None,
          "observed_common_causes": None,
          "effect_modifiers": None,
      }
  
file_path: dowhy/dowhy/causal_estimators/regression_estimator.py
imports: from typing import Any, List, Optional, Union
  import numpy
  import pandas
  import statsmodels.api
  from dowhy.causal_estimator import CausalEstimate, CausalEstimator, IdentifiedEstimand
  from dowhy.utils.encoding import one_hot_encode
label_desc: Use image node_id 2 for calling the RegressionEstimator obj's underlying member method code with example usage: obj.reset_encoders() without return types