Riccomagno, Eva and Smith, J. Q. (2007) The causal manipulation of chain event graphs. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2007 (No.14).

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Abstract

Discrete Bayesian Networks (BN’s) 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.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Graphical modeling (Statistics), Bayesian statistical decision theory
Series Name:	Working papers
Publisher:	University of Warwick. Centre for Research in Statistical Methodology
Place of Publication:	Coventry
Official Date:	2007
Dates:	Date Event 2007 Published
Date of first compliant deposit:	1 August 2016
Volume:	Vol.2007
Number:	No.14
Number of Pages:	49
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access

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