The causal manipulation of chain event graphs

Details The causal manipulation of chain event graphs The causal manipulation of chain event graphs

2007-09-21

arxiv.org/abs/0709.3380v1

Statistics

Methodology

49 pages, 18 figures

Scientific paper

Discrete Bayesian Networks have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical models called the chain event graph (CEG) models, that generalises the class of discrete BN models. It provides a flexible and expressive framework for representing and analysing the implications of causal hypotheses, expressed in terms of the effects of a manipulation of the generating underlying system. We prove that, as for a BN, identifiability analyses of causal effects can be performed through examining the topology of the CEG graph, leading to theorems analogous to the back-door theorem for the BN.

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