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Modeling Causal Reinforcement and Undermining with Noisy-AND Trees

  • Y. Xiang
  • N. Jia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)

Abstract

When data are insufficient to support learning, causal modeling, such as noisy-OR, aids elicitation by reducing probability parameters to be acquired in constructing a Bayesian network. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider their interactions from the perspective of reinforcement or undermining. We show that none of them can represent both interactions. We present the first explicit causal model that can encode both reinforcement and undermining and we show how to use such a model to support efficient probability elicitation.

Keywords

Bayesian Network Root Node Causal Event Causal Model Independence Assumption 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Y. Xiang
    • 1
  • N. Jia
    • 1
  1. 1.University of GuelphCanada

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