code: def continuous_treatment_model(
      data, covariates, treatment, variable_types
  ):
      data, covariates = binarize_discrete(
          data, covariates, variable_types
      )
      if len(data) > 300 or len([treatment] + covariates) >= 3:
          defaults = EstimatorSettings(n_jobs=4, efficient=True)
      else:
          defaults = EstimatorSettings(n_jobs=-1, efficient=False)
  
      if "c" not in variable_types.values():
          bw = "cv_ml"
      else:
          bw = "normal_reference"
  
      indep_type = get_type_string(covariates, variable_types)
      dep_type = get_type_string([treatment], variable_types)
  
      model = KDEMultivariateConditional(
          endog=data[treatment],
          exog=data[covariates],
          dep_type="".join(dep_type),
          indep_type="".join(indep_type),
          bw=bw,
          defaults=defaults,
      )
      scores = model.pdf(
          endog_predict=data[treatment], exog_predict=data[covariates]
      )
      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 5 for calling a global function with example usage: continuous_treatment_model(data, covariates, treatment, variable_types) and returns: scores