CAUSAL DISCOVERY FROM MARKOV PROPERTIES UNDER LATENT CONFOUNDERS

Abstract

We address the problems of causal structure reconstruction given conditional independence facts when latent confounders are allowed. We examine the conditions that allow one to partially or fully identify authentic causal links and latent confounders. The updated implicative rules for orienting edges under confounding are suggested. As demonstrated, it is possible to construct the new rules, which can reveal confounded causal edges and bows. The rules rely on facts of the absence of certain authentic edges (such facts may be justified by non-independence constraints, like Verma constraint, or subject-based requirements). Keywords: causal relation, d-separation, conditional independence, latent confounder, edge orientation, bow (arc).