Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
The Art of Causal Conjecture
A general identification condition for causal effects
Eighteenth national conference on Artificial intelligence
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Conditional independence and chain event graphs
Artificial Intelligence
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Causal analysis with Chain Event Graphs
Artificial Intelligence
Probabilistic evaluation of sequential plans from causal models with hidden variables
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Refining a Bayesian Network using a Chain Event Graph
International Journal of Approximate Reasoning
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We present the Chain Event Graph (CEG) as a complementary graphical model to the Causal Bayesian Network for the representation and analysis of causally manipulated asymmetric problems. Our focus is on causal identifiability - finding conditions for when the effects of a manipulation can be estimated from a subset of events observable in the unmanipulated system. CEG analogues of Pearl@?s Back Door and Front Door theorems are presented, applicable to the class of singular manipulations, which includes both Pearl@?s basic Do intervention and the class of functional manipulations possible on Bayesian Networks. These theorems are shown to be more flexible than their Bayesian Network counterparts, both in the types of manipulation to which they can be applied, and in the nature of the conditioning sets which can be used.