Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Qualitative reasoning with imprecise probabilities
Journal of Intelligent Information Systems - Special issue: fuzzy logic and uncertainty management in information systems
Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
Predicting causality ascriptions from background knowledge: model and experimental validation
International Journal of Approximate Reasoning
A Comparative Study of Six Formal Models of Causal Ascription
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Background default knowledge and causality ascriptions
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Qualitative and quantitative conditions for the transitivity of perceived causation
Annals of Mathematics and Artificial Intelligence
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If Acaused Band Bcaused C, did Acaused C? Although causality is generally regarded as transitive, some philosophers have questioned this assumption, and models of causality in artificial intelligence are often agnostic with respect to transitivity: They define causation, then check whether the definition makes all, or only some, causal arguments transitive. We consider two formal models of observation-based causation, which differ in the way they represent uncertainty. The quantitative model uses a standard probabilistic definition; the qualitative model uses a definition based on nonmonotonic consequence. The two models identify different sufficient conditions for the transitivity of causation: The Markov condition on events for the quantitative model, and a Saliency condition (if Bis true then generally Ais true) for the qualitative model. We explore the formal relations between these sufficient conditions, and between the underlying definitions of observation-based causation. These connections shed light on the range of applicability of both models.