Artificial Intelligence - Special issue on relevance
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Defining explanation in probabilistic systems
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Reasoning with cause and effect
AI Magazine
Complexity results for structure-based causality
Artificial Intelligence
Complexity results for explanations in the structural-model approach
Artificial Intelligence
Causes and explanations in the structural-model approach: tractable cases
Artificial Intelligence
Ontology-based inference for causal explanation
Integrated Computer-Aided Engineering
Approximate lineage for probabilistic databases
Proceedings of the VLDB Endowment
Deriving epistemic conclusions from agent architecture
Proceedings of the 12th Conference on Theoretical Aspects of Rationality and Knowledge
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
Responsibility and blame: a structural-model approach
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Causes and explanations revisited
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Complexity results for structure-based causality
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Ontology-based inference for causal explanation
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
A formalism for causal explanations with an answer set programming translation
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Sensitivity analysis and explanations for robust query evaluation in probabilistic databases
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Causes and explanations in the structural-model approach
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Causes and explanations: a structural-model approach: part i: causes
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Probabilistic reasoning about actions in nonmonotonic causal theories
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Structure-based causes and explanations in the independent choice logic
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Configurations for inference between causal statements
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
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We propose a new definition of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion paper. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from the literature.