Causal Inference on Discrete Data via Estimating Distance Correlations

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 and the conditional distribution mapping cause to effect as independent random variables, we propose to infer the causal direction by comparing the distance correlation between and with the distance correlation between and ⁠. We infer that X causes Y if the dependence coefficient between and is smaller. Experiments are performed to show the performance of the proposed method.