  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.
