Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: 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.

A Bayesian Approach to Learning Causal Networks