Causal Inference on Discrete Data via Estimating Distance Correlations
  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.
