title: Causal Inference on Discrete Data via Estimating Distance Correlations.
abstract: In 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 [Formula: see text] and the conditional distribution mapping cause to effect [Formula: see text] as independent random variables, we propose to infer the causal direction by comparing the distance correlation between [Formula: see text] and [Formula: see text] with the distance correlation between [Formula: see text] and [Formula: see text]. We infer that X causes Y if the dependence coefficient between [Formula: see text] and [Formula: see text] is smaller. Experiments are performed to show the performance of the proposed method.
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 999077, Hong Kong frliu@cse.cuhk.edu.hk.
authors.forename: Furui
authors.initials: F
authors.lastname: Liu
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 999077, Hong Kong lwchan@cse.euhk.edu.hk.
authors.forename: Laiwan
authors.initials: L
authors.lastname: Chan