Uncertainty in Artificial Intelligence
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A Definition and Graphical Representation for Causality
David Heckerman, Ross Shachter
Abstract:
We present a precise definition of cause and effect in terms of a fundamental notion called unresponsiveness. Our definition is based on Savage's (1954) formulation of decision theory and departs from the traditional view of causation in that our causal assertions are made relative to a set of decisions. An important consequence of this departure is that we can reason about cause locally, not requiring a causal explanation for every dependency. Such local reasoning can be beneficial because it may not be necessary to determine whether a particular dependency is causal to make a decision. Also in this paper, we examine the graphical encoding of causal relationships. We show that influence diagrams in canonical form are an accurate and efficient representation of causal relationships. In addition, we establish a correspondence between canonical form and Pearl's causal theory.
Keywords: Causality, causal model, causal theory, causal networks, influence diagrams, canonic
Pages: 262-273
PS Link: http://www.research.microsoft.com/research/dtg/heckerma/TR-94-11.htm
PDF Link: /papers/95/p262-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman95,
AUTHOR = "David Heckerman and Ross Shachter",
TITLE = "A Definition and Graphical Representation for Causality",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "262--273"
}


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