Causal analysis with Chain Event Graphs

  • Authors:
  • Peter Thwaites;Jim Q. Smith;Eva Riccomagno

  • Affiliations:
  • Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom;Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom;Department of Mathematics, Universití degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2010

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Abstract

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.