Uncertainty in Artificial Intelligence
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A New Look at Causal Independence
David Heckerman, John Breese
Abstract:
Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction where (1) the effect is independent of the order that causes are introduced and (2) the impact of a single cause on the effect does not depend on what other causes have previously been applied. In this paper, we introduce an equivalent a temporal characterization of causal independence based on a functional representation of the relationship between causes and the effect. In this representation, the interaction between causes and effect can be written as a nested decomposition of functions. Causal independence can be exploited by representing this decomposition in the belief network, resulting in representations that are more efficient for inference than general causal models. We present empirical results showing the benefits of a causal-independence representation for belief-network inference.
Keywords: Bayesian networks, causal independence, knowledge acquisition, inference.
Pages: 286-292
PS Link: ftp://research.microsoft.com/pub/Tech-Reports/Spring94/TR-94-08.PS.Z
PDF Link: /papers/94/p286-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman94,
AUTHOR = "David Heckerman and John Breese",
TITLE = "A New Look at Causal Independence",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "286--292"
}


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