More complicated questions about maxima and minima, and some closures of NP
International Colloquium on Automata, Languages and Programming on Automata, languages and programming
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
A catalog of complexity classes
Handbook of theoretical computer science (vol. A)
Complexity classes defined by counting quantifiers
Journal of the ACM (JACM)
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Probabilistic evaluation of counterfactual queries
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Provably correct theories of action
Journal of the ACM (JACM)
PP is closed under intersection
Selected papers of the 23rd annual ACM symposium on Theory of computing
On the hardness of approximate reasoning
Artificial Intelligence
Artificial Intelligence - Special issue on relevance
An algorithm to evaluate quantified Boolean formulae
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An action language based on causal explanation: preliminary report
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A logic of universal causation
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic (TOCL)
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
Improvements to the Evaluation of Quantified Boolean Formulae
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Reasoning with Cause and Effect
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Causes and Explanations: A Structural-Model Approach: Part 1: Causes
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A Distributed Algorithm to Evaluate Quantified Boolean Formulae
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
SATO: An Efficient Propositional Prover
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
Strategies for determining causes of events
Eighteenth national conference on Artificial intelligence
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Causes and explanations: a structural-model approach-part II: explanations
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Causal theories of action and change
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Embracing causality in specifying the indeterminate effects of actions
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Causes and explanations in the structural-model approach
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in 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
What causes a system to satisfy a specification?
ACM Transactions on Computational Logic (TOCL)
Responsibility and blame: a structural-model approach
Journal of Artificial Intelligence Research
Responsibility and blame: a structural-model approach
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Causes and explanations in the structural-model approach: Tractable cases
Artificial Intelligence
The complexity of causality and responsibility for query answers and non-answers
Proceedings of the VLDB Endowment
Tracing data errors with view-conditioned causality
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
From probabilistic counterexamples via causality to fault trees
SAFECOMP'11 Proceedings of the 30th international conference on Computer safety, reliability, and security
Structure-based causes and explanations in the independent choice logic
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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We give a precise picture of the computational complexity of causal relationships in Pearl's structural models, where we focus on causality between variables, event causality, and probabilistic causality. As for causality between variables, we consider the notions of causal irrelevance, cause, cause in a context, direct cause, and indirect cause. As for event causality, we analyze the complexity of the notions of necessary and possible cause, and of the sophisticated notions of weak and actual cause by Halpern and Pearl. In the course of this, we also prove an open conjecture by Halpern and Pearl, and establish other semantic results. We then analyze the complexity of the probabilistic notions of probabilistic causal irrelevance, likely causes of events, and occurrences of events despite other events. Moreover, we consider decision and optimization problems involving counterfactual formulas. To our knowledge, no complexity aspects of causal relationships in the structural-model approach have been considered so far, and our results shed light on this issue.