Artificial Intelligence - Special issue on relevance
First-Order Dynamic Logic
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Defining explanation in probabilistic systems
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Probabilities of causation: Bounds and identification
Annals of Mathematics and Artificial Intelligence
Reasoning with cause and effect
AI Magazine
A new characterization of the experimental implications of causal Bayesian networks
Eighteenth national conference on Artificial intelligence
Probabilities of causation: bounds and identification
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
A calculus for causal relevance
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Local characterizations of causal bayesian networks
GKR'11 Proceedings of the Second international conference on Graph Structures for Knowledge Representation and Reasoning
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Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutions to the equations are unique, (3) arbitrary theories (where the equations may not have solutions and, if they do, they are not necessarily unique). It is shown that to reason about causality in the most general third class, we must extend the language used by Galles and Pearl. In addition, the complexity of the decision procedures is examined for all the languages and classes of models considered.