Causal Discovery on Discrete Data with Extensions to Mixture Model
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AbstractIn this article, we deal with the causal discovery problem on discrete data. First, we present a causal discovery method for traditional additive noise models that identifies the causal direction by analyzing the supports of the conditional distributions. Then, we present a causal mixture model to address the problem that the function transforming cause to effect varies across the observations. We propose a novel method called Support Analysis (SA) for causal discovery with the mixture model. Experiments using synthetic and real data are presented to demonstrate the performance of our proposed algorithm.
All Author(s) ListLiu FR, Chan LW
Journal nameACM Transactions on Intelligent Systems and Technology
Year2016
Month1
Day1
Volume Number7
Issue Number2
PublisherASSOC COMPUTING MACHINERY
ISSN2157-6904
eISSN2157-6912
LanguagesEnglish-United Kingdom
KeywordsAlgorithms; Causal discovery; discrete; mixture; Theory
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Information Systems

Last updated on 2020-28-07 at 04:16