code: def binary_treatment_model(
      data, covariates, treatment, variable_types
  ):
      data, covariates = binarize_discrete(
          data, covariates, variable_types
      )
      model = LogisticRegression(solver="lbfgs")
      model = model.fit(data[covariates], data[treatment])
      scores = model.predict_proba(data[covariates])
      scores = scores[
          range(len(scores)), data[treatment].values.astype(int)
      ]
      return scores
  
file_path: dowhy/dowhy/utils/propensity_score.py
imports: import numpy
  import pandas
  from pandas import get_dummies
  from sklearn.linear_model import LogisticRegression
  from sklearn.preprocessing import LabelEncoder
  from statsmodels.nonparametric.kernel_density import EstimatorSettings, KDEMultivariateConditional
label_desc: Use image node_id 3 for calling a global function with example usage: binary_treatment_model(data, covariates, treatment, variable_types) and returns: scores