A Bayesian Approach to Learning Causal Networks

Understand

Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships.

• In this paper, we examine Bayesian methods for learning both types of networks.
• Bayesian methods for learning acausal networks are fairly well developed.
• These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence.