Uncertainty in Artificial Intelligence
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Explanation Trees for Causal Bayesian Networks
Ulf Nielsen, Jean-Philippe Pellet, André Elisseeff
Abstract:
Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.
Keywords:
Pages: 427-434
PS Link:
PDF Link: /papers/08/p427-nielsen.pdf
BibTex:
@INPROCEEDINGS{Nielsen08,
AUTHOR = "Ulf Nielsen and Jean-Philippe Pellet and AndrĂ© Elisseeff",
TITLE = "Explanation Trees for Causal Bayesian Networks",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "427--434"
}


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