The causal manipulation of chain event graphs

Eva Riccomagno; Jim Q. Smith

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arXiv · 0709.3380

The causal manipulation of chain event graphs

Eva Riccomagno·Jim Q. Smith

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Abstract

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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BibTeX RIS

Eva Riccomagno, Jim Q. Smith. 2007-09-21. The causal manipulation of chain event graphs. https://arxiv.org/abs/0709.3380

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