Explanation Trees for Causal Bayesian Networks

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 sal 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.

[1]  正人 木村.  Max-Planck-Institute for Mathematics in the Sciences(海外,ラボラトリーズ) , 2001 .

[2]  A. Elisseeff,et al.  On Causal Explanations in Bayesian Networks , 2007 .

[3]  Richard Scheines,et al.  Causation, Prediction, and Search, Second Edition , 2000, Adaptive computation and machine learning.

[4]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[5]  Brendan J. Frey,et al.  Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.

[6]  María Julia Flores Gallego.  Bayesian networks inference advanced algorithms for triangulation abd partical abduction , 2005 .

[7]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[8]  José A. Gámez,et al.  Simplifying Explanations in Bayesian Belief Networks , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[9]  Joseph Y. Halpern,et al.  Causes and Explanations: A Structural-Model Approach. Part II: Explanations , 2001, The British Journal for the Philosophy of Science.

[10]  Solomon Eyal Shimony.  Explanation, Irrelevance, and Statistical Independence , 1991, AAAI.

[11]  James D. Park,et al.  MAP Complexity Results and Approximation Methods , 2002, UAI.

[12]  Daniel Polani,et al.  Information Flows in Causal Networks , 2008, Adv. Complex Syst..

[13]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[14]  Marek J. Druzdzel,et al.  Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning , 1990, UAI.

[15]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[16]  Joseph Y. Halpern,et al.  Defining Explanation in Probabilistic Systems , 1997, UAI.

[17]  Joseph Y. Halpern,et al.  Causes and explanations: A structural-model approach , 2000 .

[18]  Carmen Lacave,et al.  A review of explanation methods for Bayesian networks , 2002, The Knowledge Engineering Review.

[19]  P. Spirtes,et al.  Causation, Prediction, and Search, 2nd Edition , 2001 .

[20]  J. Pearl.  Causal diagrams for empirical research , 1995 .

[21]  Adnan Darwiche,et al.  On the Robustness of Most Probable Explanations , 2006, UAI.

[22]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[23]  L. M. M.-T..  Theory of Probability , 1929, Nature.