Uncertainty in Artificial Intelligence
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Axiomatizing Causal Reasoning
Joseph Halpern
Abstract:
Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutions to the equations are unique, (3) arbitrary theories (where the equations may not have solutions and, if they do, they are not necessarily unique). It is shown that to reason about causality in the most general third class, we must extend the language used by Galles and Pearl. In addition, the complexity of the decision procedures is examined for all the languages and classes of models considered.
Keywords: Causal reasoning, structural equations, axiomatization.
Pages: 202-210
PS Link: http://www.cs.cornell.edu/home/halpern/papers/cstruc.ps
PDF Link: /papers/98/p202-halpern.pdf
BibTex:
@INPROCEEDINGS{Halpern98,
AUTHOR = "Joseph Halpern ",
TITLE = "Axiomatizing Causal Reasoning",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "202--210"
}


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