Parameter identification in linear non-Gaussian causal models under
general confounding
Parameter identification in linear non-Gaussian causal models under
general confounding
Linear non-Gaussian causal models postulate that each random variable is a linear function of parent variables and non-Gaussian exogenous error terms. We study identification of the linear coefficients when such models contain latent variables. Our focus is on the commonly studied acyclic setting, where each model corresponds to a directed …