Causal analysis with chain event graphs
Smith, J. Q., 1953-, Riccomagno, Eva and Thwaites, Peter (2010) Causal analysis with chain event graphs. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. (Working papers, Vol.2009).
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Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
As the Chain Event Graph (CEG) has a topology which represents sets of conditional independence statements, it becomes especially useful when problems lie naturally in a discrete asymmetric non-product space domain, or when much context-specific information is present. In this paper we show that it can also be a powerful representational tool for a wide variety of causal hypotheses in such domains. Furthermore, we demonstrate that, as with Causal Bayesian Networks (CBNs), the identifiability of the effects of causal manipulations when observations of the system are incomplete can be verified simply by reference to the topology of the CEG. We close the paper with a proof of a Back Door Theorem for CEGs, analogous to Pearl's Back Door Theorem for CBNs.
|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|
|Journal or Publication Title:||ARTIFICIAL INTELLIGENCE|
|Publisher:||University of Warwick. Centre for Research in Statistical Methodology|
|Place of Publication:||Coventry|
|Number of Pages:||37|
|Page Range:||pp. 889-909|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
|Funder:||Engineering and Physical Sciences Research Council (EPSRC)|
|Grant number:||EP/F036752/1 (EPSRC)|
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