Causal Inference on Discrete Data via Estimating Distance Correlations
Furui Liu, Laiwan Chan · Neural Computation · 2016
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.