In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain. By considering the distribution of the cause P (X) and the conditional distribution mapping cause to effect P (Y |X) as independent random variables, we propose to infer the causal direction via comparing the distance correlation between P (X) and P (Y |X) with the distance correlation between P (Y ) and P (X|Y ). We infer "X causes Y " if the dependence coefficient between P (X) and P (Y |X) is smaller. Experiments are performed to show the performance of the proposed method.
Liu and Chan implemented this framework by calculating the distance correlation for discrete data between P (X) and P (Y | X) REF .
Causal Inference on Discrete Data via Estimating Distance Correlations
Neural Computation, Vol. 28, No. 5, 2016
Medicine Computer Science Mathematics