class_docstr: A sklearn style wrapper class for statsmodels' OLS.
class_docstr_tok: ["A","sklearn","style","wrapper","class","for","statsmodels","'","OLS","."]
docstr: Initialize a statsmodels' OLS wrapper class object.
  Args:
      cov_type (str, optional): covariance estimator type.
      alpha (float, optional): the confidence level alpha.
docstr_tok: ["Initialize","a","statsmodels","'","OLS","wrapper","class","object",".","Args",":","cov_type","(","str",",","optional",")",":","covariance","estimator","type",".","alpha","(","float",",","optional",")",":","the","confidence","level","alpha","."]
code: def __init__(self, cov_type="HC1", alpha=0.05):
      """Initialize a statsmodels' OLS wrapper class object.
      Args:
          cov_type (str, optional): covariance estimator type.
          alpha (float, optional): the confidence level alpha.
      """
      self.cov_type = cov_type
      self.alpha = alpha
  
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 1 to create a new StatsmodelsOLS object  with example: obj = StatsmodelsOLS(cov_type, alpha)