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Explanation Trees for Causal Bayesian Networks
This video was recorded at 24th Conference on Uncertainty in Artificial Intelligence (UAI), Helsinki 2008. 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 flow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
