A Bayesian Approach to Learning Causal Networks

doi:10.1017/CBO9780511611308.012

11 - A Bayesian Approach to Learning Causal Networks

Published online by Cambridge University Press: 05 June 2012

David Heckerman

Edited by

Ward Edwards ,

Ralph F. Miles Jr. and

Detlof von Winterfeldt

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David Heckerman: Affiliation:
Machine Learning and Applied Statistics Group, Microsoft Research
Ralph F. Miles Jr.: Affiliation:
California Institute of Technology
Detlof von Winterfeldt: Affiliation:
University of Southern California

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Summary

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.

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.

Type: Chapter
Information: Advances in Decision Analysis
From Foundations to Applications
, pp. 202 - 220

DOI: https://doi.org/10.1017/CBO9780511611308.012 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2007

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