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.

def est_propensity(self, lin='all', qua=None):

		"""
		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.
		"""

		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()