docstr: Calculates and plots feature importances for each treatment group, based on specified method in __init__.
  Skips the calculation part if importance_dict is given.
  Args:
      importance_dict (optional, dict): a dict of feature importance matrics. If None, importance_dict will be computed.
      title_prefix (optional, str): a prefix to the title of the plot.
      figsize (optional, tuple): the size of the figure.
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code: def plot_importance(
      self, importance_dict=None, title_prefix="", figsize=(12, 8)
  ):
      """
      Calculates and plots feature importances for each treatment group, based on specified method in __init__.
      Skips the calculation part if importance_dict is given.
      Args:
          importance_dict (optional, dict): a dict of feature importance matrics. If None, importance_dict will be computed.
          title_prefix (optional, str): a prefix to the title of the plot.
          figsize (optional, tuple): the size of the figure.
      """
      if importance_dict is None:
          importance_dict = self.get_importance()
      for group, series in importance_dict.items():
          plt.figure()
          series.sort_values().plot(kind="barh", figsize=figsize)
          title = group
          if title_prefix != "":
              title = "{} - {}".format(title_prefix, title)
          plt.title(title)
  
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 9 for calling the Explainer obj's underlying member method code with example usage: obj.plot_importance(importance_dict, title_prefix, figsize) without return types