Uncertainty in Artificial Intelligence
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A Calculus for Causal Relevance
Blai Bonet
Abstract:
This paper presents a sound and completecalculus for causal relevance, based onPearl's functional models semantics.The calculus consists of axioms and rulesof inference for reasoning about causalrelevance relationships.We extend the set of known axioms for causalrelevance with three new axioms, andintroduce two new rules of inference forreasoning about specific subclasses ofmodels.These subclasses give a more refinedcharacterization of causal models than the one given in Halpern's axiomatizationof counterfactual reasoning.Finally, we show how the calculus for causalrelevance can be used in the task ofidentifying causal structure from non-observational data.
Keywords:
Pages: 40-47
PS Link:
PDF Link: /papers/01/p40-bonet.pdf
BibTex:
@INPROCEEDINGS{Bonet01,
AUTHOR = "Blai Bonet ",
TITLE = "A Calculus for Causal Relevance",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "40--47"
}


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