RG Cowell and JQ Smith
Causal discovery through MAP selection of stratified chain event graphs
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 a such a dataset, with a view to deducing the causal structure of the variables. 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.
Keywords: Causality; chain event graph; event tree; stratified chain event graph; staged event tree; structural learning; MAP estimation.