code: def est_propensity(self, lin='all', qua=None):
  
  		
  
  		lin_terms = parse_lin_terms(self.raw_data['K'], lin)
  		qua_terms = parse_qua_terms(self.raw_data['K'], qua)
  
  		self.propensity = Propensity(self.raw_data, lin_terms, qua_terms)
  		self.raw_data._dict['pscore'] = self.propensity['fitted']
  		self._post_pscore_init()
docs: Estimates the propensity scores given list of covariates to
  		include linearly or quadratically.
  
  		The propensity score is the conditional probability of
  		receiving the treatment given the observed covariates.
  		Estimation is done via a logistic regression.
  
  		Parameters
  		----------
  		lin: string or list, optional
  			Column numbers (zero-based) of variables of
  			the original covariate matrix X to include
  			linearly. Defaults to the string 'all', which
  			uses whole covariate matrix.
  		qua: list, optional
  			Tuples indicating which columns of the original
  			covariate matrix to multiply and include. E.g.,
  			[(1,1), (2,3)] indicates squaring the 2nd column
  			and including the product of the 3rd and 4th
  			columns. Default is to not include any
  			quadratic terms.