docstr: Calculates feature importances for each treatment group, based on the model_tau's default implementation.
docstr_tok: ["Calculates","feature","importances","for","each","treatment","group",",","based","on","the","model_tau","'s","default","implementation","."]
code: def default_importance(self):
      """
      Calculates feature importances for each treatment group, based on the model_tau's default implementation.
      """
      importance_dict = {}
      if self.r_learners is not None:
          self.models_tau = deepcopy(self.r_learners)
      for group, idx in self.classes.items():
          importance_dict[group] = self.models_tau[
              group
          ].feature_importances_
          if self.normalize:
              importance_dict[group] = (
                  importance_dict[group] / importance_dict[group].sum()
              )
  
      return importance_dict
  
imports: import pandas
  import shap
  import matplotlib.pyplot
  from lightgbm import LGBMRegressor
  from sklearn.inspection import permutation_importance
  from sklearn.model_selection import train_test_split
  from copy import deepcopy
  from causalml.inference.meta.utils import convert_pd_to_np
label_desc: Use image node_id 6 for calling the Explainer obj's underlying member method code with example usage: obj.default_importance() and returns: importance_dict