Open Access
2014 Causal discovery through MAP selection of stratified chain event graphs
Robert G. Cowell, James Q. Smith
Electron. J. Statist. 8(1): 965-997 (2014). DOI: 10.1214/14-EJS917

Abstract

We introduce a subclass of chain event graphs that we call stratified chain event graphs, and present a dynamic programming algorithm for the optimal selection of such chain event graphs that maximizes a decomposable score derived from a complete independent sample. We apply the algorithm to such a dataset, with a view to deducing the causal structure of the variables under the hypothesis that there are no unobserved confounders. We show that the algorithm is suitable for small problems. Similarities with and differences to a dynamic programming algorithm for MAP learning of Bayesian networks are highlighted, as are the relations to causal discovery using Bayesian networks.

Citation

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Robert G. Cowell. James Q. Smith. "Causal discovery through MAP selection of stratified chain event graphs." Electron. J. Statist. 8 (1) 965 - 997, 2014. https://doi.org/10.1214/14-EJS917

Information

Published: 2014
First available in Project Euclid: 29 July 2014

zbMATH: 1349.62299
MathSciNet: MR3263109
Digital Object Identifier: 10.1214/14-EJS917

Subjects:
Primary: 62F15
Secondary: 62-07

Keywords: causality , chain event graph , event tree , MAP estimation , staged event tree , stratified chain event graph , structural learning

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.8 • No. 1 • 2014
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