A Bayesian Approach to Learning Causal Networks
David E. Heckerman · Cambridge University Press eBooks · 2007
. 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. There has been a great deal of recent interest in Bayesian methods for learning Bayesian networks from data (Spiegelhalter and Lauritzen 1990; Cooper and Herskovits 1991, 1992; Buntine 1991, 1994; Spiegelhalter, Dawid, Lauritzen and Cowell 1993; Madigan and Raftery 1994; Heckerman et al. 1994, 1995). These methods take prior knowledge of a domain and statistical data and construct one or more Bayesian-network models of the domain. Most of this work has concentrated on Bayesian networks interpreted as a representation of probabilistic conditional independence. Nonetheless, several researchers have proposed a causal interpretation for Bayesian networks (Pearl and Verma 1991; Spirtes, Glymour, and Scheines 1993; Heckerman and Shachter 1995). These researchers show that having a causal interpretation can be important because it allows us to predict the effects of interventions in a domain – something that cannot be done without a causal interpretation. In this paper, we extend Bayesian methods for learning acausal Bayesian networks to causal Bayesian-networks learning.