Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic evaluation of counterfactual queries
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A Bayesian approach to learning causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning and diagnosis in manufacturing processes through an executable Bayesian network
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Reasoning with cause and effect
AI Magazine
A new characterization of the experimental implications of causal Bayesian networks
Eighteenth national conference on Artificial intelligence
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Learning probabilistic networks
The Knowledge Engineering Review
Identifying conditional causal effects
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Identifying linear causal effects
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A characterization of interventional distributions in semi-Markovian causal models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning
The Journal of Machine Learning Research
A Clinician's tool for analyzing non-compliance
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Bayesian networks for dependability analysis: an application to digital control reliability
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Instrumentality tests revisited
UAI'01 Proceedings of the Seventeenth 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
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Defining explanation in probabilistic systems
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Decision-theoretic troubleshooting: a framework for repair and experiment
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A graph-theoretic analysis of information value
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Causal bounds and observable constraints for non-deterministic models
The Journal of Machine Learning Research
Formulating Asymmetric Decision Problems as Decision Circuits
Decision Analysis
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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We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.