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 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.