nips nips2007 nips2007-198 nips2007-198-reference knowledge-graph by maker-knowledge-mining

198 nips-2007-The Noisy-Logical Distribution and its Application to Causal Inference

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Author: Hongjing Lu, Alan L. Yuille

Abstract: We describe a novel noisy-logical distribution for representing the distribution of a binary output variable conditioned on multiple binary input variables. The distribution is represented in terms of noisy-or’s and noisy-and-not’s of causal features which are conjunctions of the binary inputs. The standard noisy-or and noisy-andnot models, used in causal reasoning and artificial intelligence, are special cases of the noisy-logical distribution. We prove that the noisy-logical distribution is complete in the sense that it can represent all conditional distributions provided a sufficient number of causal factors are used. We illustrate the noisy-logical distribution by showing that it can account for new experimental findings on how humans perform causal reasoning in complex contexts. We speculate on the use of the noisy-logical distribution for causal reasoning and artificial intelligence.

reference text

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