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Bayesian Constraint-based Causal Discovery (BCCD) is a state-of-the-art method for robust causal discovery in the presence of latent variables. It combines probabilistic estimation of Bayesian networks over subsets of variables with a causal logic to infer causal statements. Currently BCCD is limited to discrete or Gaussian variables. Most of the real-world(More)
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly(More)
etc. are disjoint (sets of) observed nodes in a causal DAG G C , and S represents the (possibly empty) set of selection nodes. 2 Background The definition of a causal relation in a causal DAG, rewritten in terms of standard logical properties: Proposition 1. Causal relations in a DAG G C are: Proof. As the edges in G C represent causal relations, a path of(More)