A Bayesian Approach to Learning Causal Networks

This paper introduces two sufficient assumptions, called mechanism independence and component independence, and shows that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow methods for learning acausal networks to learn causal networks.

Abstract

. Bayesian methods have been developed for learning Bayesian networks from data. Most of this work has concentrated on Bayesian networks interpreted as a representation of probabilistic conditional independence without considering causation. Other researchers have shown that having a causal interpretation can be important because it allows us to predict the effects of interventions in a domain. In this chapter, we extend Bayesian methods for learning acausal Bayesian networks to causal Bayesian networks.