The Art of Causal Conjecture
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Probabilistic decision graphs-combining verification and AI techniques for probabilistic inference
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Learning Bayesian Networks
Conditional independence and chain event graphs
Artificial Intelligence
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Learning probabilistic decision graphs
International Journal of Approximate Reasoning
Structural-EM for learning PDG models from incomplete data
International Journal of Approximate Reasoning
Causal analysis with Chain Event Graphs
Artificial Intelligence
Bayesian MAP model selection of chain event graphs
Journal of Multivariate Analysis
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning recursive probability trees from probabilistic potentials
International Journal of Approximate Reasoning
Causal identifiability via Chain Event Graphs
Artificial Intelligence
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The search for a useful explanatory model based on a Bayesian Network (BN) now has a long and successful history. However, when the dependence structure between the variables of the problem is asymmetric then this cannot be captured by the BN. The Chain Event Graph (CEG) provides a richer class of models which incorporates these types of dependence structures as well as retaining the property that conclusions can be easily read back to the client. We demonstrate on a real health study how the CEG leads us to promising higher scoring models and further enables us to make more refined conclusions than can be made from the BN. Further we show how these graphs can express causal hypotheses about possible interventions that could be enforced.