Whereas acausal Bayesian networks rep resent probabilistic independence, causal Bayesian networks represent causal relation ships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal net works are fairly well developed. These meth ods often employ assumptions to facilitate the construction of priors, including the as sumptions of parameter independence, pa rameter modularity, and likelihood equiva lence. We show that although these assump tions also can be appropriate for learning causal networks, we need additional assump tions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component in dependence. We show that these new as sumptions, when combined with parame ter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.
Reference REF proposes an extension for Bayesian methods those were used to learn acausal Bayesian network to learn causal Bayesian network; always respecting Pearl's causal-theory.
A Bayesian Approach to Learning Causal Networks
ArXiv
ArXiv
Computer Science