Causal identifiability via Chain Event Graphs
Abstract: We present the Chain Event Graph (CEG) as a complementary graphical model to the Causal Bayesian Network for the representation and analysis of causally manipulated asymmetric problems. CEG analogues of Pearl's Back Door and Front Door theorems are presented, applicable to the class of singular manipulations, which includes both Pearl's basic Do intervention and the class of functional manipulations possible on Bayesian Networks. These theorems are shown to be more flexible than their Bayesian Network counterparts, both in the types of manipulation to which they can be applied, and in the nature of the conditioning sets which can be used.
Keywords: Back Door theorem, Bayesian Network, causal identifiablity, causal manipulation, Chain Event Graph, conditional independence, Front Door theorem.