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.

def est_propensity_s(self, lin_B=None, C_lin=1, C_qua=2.71):
	
		"""
		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.
		"""

		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()