Uncertainty in Artificial Intelligence
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A Decision-Based View of Causality
David Heckerman, Ross Shachter
Abstract:
Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able to predict the effects of actions. In this paper, we attempt to unite two branches of research that address such predictions: causal modeling and decision analysis. First, we provide a definition of causal dependence in decision-analytic terms, which we derive from consequences of causal dependence cited in the literature. Using this definition, we show how causal dependence can be represented within an influence diagram. In particular, we identify two inadequacies of an ordinary influence diagram as a representation for cause. We introduce a special class of influence diagrams, called causal influence diagrams, which corrects one of these problems, and identify situations where the other inadequacy can be eliminated. In addition, we describe the relationships between Howard Canonical Form and existing graphical representations of cause.
Keywords: Causality, influence diagrams, Howard Canonical Form, counterfactual reasoning, causa
Pages: 302-310
PS Link:
PDF Link: /papers/94/p302-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman94,
AUTHOR = "David Heckerman and Ross Shachter",
TITLE = "A Decision-Based View of Causality",
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 = "302--310"
}


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