The causal manipulation of chain event graphs
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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Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
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|
|Number of Pages:||49|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
 P. Anderson and J.Q. Smith (2005). A graphical framework for representing the semantics of asymmetric
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