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)
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Probabilities of causation: Bounds and identification
Annals of Mathematics and Artificial Intelligence
A Theory of First-Order Counterfactual Reasoning
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Causation, action, and counterfactuals
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
The algorithmization of counterfactuals
Annals of Mathematics and Artificial Intelligence
Probabilities of causation: bounds and identification
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, determination of liability, and policy analysis. We present a method for evaluating counterfactuals when the underlying causal model is represented by structural models - a nonlinear generalization of the simultaneous equations models commonly used in econometrics and social sciences. This new method provides a coherent means for evaluating policies involving the control of variables which, prior to enacting the policy were influenced by other variables in the system.