Discovering causal relations is fundamental to reasoning and intelligence. In
particular, observational causal discovery algorithms estimate the cause-effect
relation between two random entities $X$ and $Y$, given $n$ samples from
$P(X,Y)$.
In this paper, we develop a framework to esti