class_docstr: A sklearn style wrapper class for statsmodels' OLS.
class_docstr_tok: ["A","sklearn","style","wrapper","class","for","statsmodels","'","OLS","."]
code: def predict(self, X):
      # Append ones. The first column is for the treatment indicator.
      X = sm.add_constant(X, prepend=False, has_constant="add")
      return self.model.predict(X)
  
imports: import logging
  import numpy
  from tqdm import tqdm
  from scipy.stats import norm
  from sklearn.dummy import DummyRegressor
  import statsmodels.api
  from copy import deepcopy
  from causalml.inference.meta.base import BaseLearner
  from causalml.inference.meta.utils import check_treatment_vector, convert_pd_to_np
  from causalml.metrics import regression_metrics, classification_metrics
label_desc: Use image node_id 3 for calling the StatsmodelsOLS obj's underlying member method code with example usage: obj.predict(X) and returns: self