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A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics
2016
Conference on Uncertainty in Artificial Intelligence
Relational Causal Models (RCM) generalize Causal Bayesian Networks so as to extend causal discovery to relational domains. We provide a novel and elegant characterization of the Markov equivalence of RCMs under path semantics. We introduce a novel representation of unshielded triples that allows us to efficiently determine whether an RCM is Markov equivalent to another. Under path semantics, we provide a sound and complete algorithm for recovering the structure of an RCM from conditional
dblp:conf/uai/LeeH16
fatcat:hgogyb6qsfea3jqn5zkpgzmxam