UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A general identification condition for causal effects
Eighteenth national conference on Artificial intelligence
Journal of Artificial Intelligence Research
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Effects of treatment on the treated: identification and generalization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Counterfactuals and policy analysis in structural models
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Utilizing cognitive mechanisms in the analysis of counterfactual conditionals by AGI systems
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
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Recent advances in causal reasoning have given rise to a computation model that emulates the process by which humans generate, evaluate and distinguish counterfactual sentences. Though compatible with the "possible worlds" account, this model enjoys the advantages of representational economy, algorithmic simplicity and conceptual clarity. Using this model, the paper demonstrates the processing of counterfactual sentences on a classical example due to Ernest Adam. It then gives a panoramic view of several applications where counterfactual reasoning has benefited problem areas in the empirical sciences.