City Research Online

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.

Publication Type:	Article
Publisher Keywords:	Causality, chain event graph, event tree, stratified chain event graph, staged event tree, structural learning, MAP estimation
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics
Departments:	Bayes Business School > Actuarial Science & Insurance
Related URLs:	Author
SWORD Depositor:	Symplectic Administrator

Export

Downloads