Causal Inference on Discrete Data via Estimating Distance Correlations
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AbstractIn this article, we deal with the problem of inferring causal directions when the data are on discrete domain. By considering the distribution of the cause P(X) and the conditional distribution mapping cause to effect P(Y vertical bar X) as independent random variables, we propose to infer the causal direction by comparing the distance correlation between P(X) and P(Y vertical bar X) with the distance correlation between P(Y) and P(X vertical bar Y). We infer that X causes Y if the dependence coefficient between P(X) and P(Y vertical bar X) is smaller. Experiments are performed to show the performance of the proposed method.
All Author(s) ListLiu FR, Chan LW
Journal nameNeural Computation
Year2016
Month5
Day1
Volume Number28
Issue Number5
PublisherMIT PRESS
Pages801 - 814
ISSN0899-7667
eISSN1530-888X
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Neurosciences; Neurosciences & Neurology

Last updated on 2020-29-07 at 00:06