code: def est_propensity_s(self, lin_B=None, C_lin=1, C_qua=2.71):
  	
  		
  
  		lin_basic = parse_lin_terms(self.raw_data['K'], lin_B)
  
  		self.propensity = PropensitySelect(self.raw_data, lin_basic,
  		                                   C_lin, C_qua)
  		self.raw_data._dict['pscore'] = self.propensity['fitted']
  		self._post_pscore_init()
docs: Estimates the propensity score with covariates selected using
  		the algorithm suggested by [1]_.
  
  		The propensity score is the conditional probability of
  		receiving the treatment given the observed covariates.
  		Estimation is done via a logistic regression.
  
  		The covariate selection algorithm is based on a sequence
  		of likelihood ratio tests.
  
  		Parameters
  		----------
  		lin_B: list, optional
  			Column numbers (zero-based) of variables of
  			the original covariate matrix X to include
  			linearly. Defaults to empty list, meaning
  			every column of X is subjected to the
  			selection algorithm.
  		C_lin: scalar, optional
  			Critical value used in likelihood ratio tests
  			to decide whether candidate linear terms should
  			be included. Defaults to 1 as in [1]_.
  		C_qua: scalar, optional
  			Critical value used in likelihood ratio tests
  			to decide whether candidate quadratic terms
  			should be included. Defaults to 2.71 as in
  			[1]_.
  
  		References
  		----------
  		.. [1] Imbens, G. & Rubin, D. (2015). Causal Inference in
  			Statistics, Social, and Biomedical Sciences: An
  			Introduction.