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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)
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)
Causal discovery provides an opportunity to infer causal relationships from purely observational data and to predict the effect of interventions. Constraint-based methods for causal discovery exploit conditional (in)dependencies to infer the direction of causal relationships. They typically work through forward chaining: given some causal statements, others(More)