Artificial Intelligence - Special issue on relevance
First-Order Dynamic Logic
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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
Complexity results for structure-based causality
Artificial Intelligence
Caveats for Causal Reasoning with Equilibrium Models
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Complexity results for explanations in the structural-model approach
Artificial Intelligence
Using counterfactuals in knowledge-based programming
Distributed Computing
Causes and explanations in the structural-model approach: tractable cases
Artificial Intelligence
Complete Identification Methods for the Causal Hierarchy
The Journal of Machine Learning Research
Identification of joint interventional distributions in recursive semi-Markovian causal models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A characterization of interventional distributions in semi-Markovian causal models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Complexity results for structure-based causality
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Identifiability of path-specific effects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Causes and explanations in the structural-model approach: Tractable cases
Artificial Intelligence
Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning
The Journal of Machine Learning Research
The algorithmization of counterfactuals
Annals of Mathematics and Artificial Intelligence
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
Structure-based causes and explanations in the independent choice logic
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A Lewisian Logic of Causal Counterfactuals
Minds and Machines
Cyclic causal models with discrete variables: Markov Chain equilibrium semantics and sample ordering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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 (1997, 1998). In addition, the complexity of the decision procedures is characterized for all the languages and classes of models considered.