  Pairwise Causal Discovery is the task of determining causal, anticausal,
confounded or independence relationships from pairs of variables. Over the last
few years, this challenging task has promoted not only the discovery of novel
machine learning models aimed at solving the task, but also discussions on how
learning the causal direction of variables may benefit machine learning
overall. In this paper, we show that Quantitative Information Flow (QIF), a
measure usually employed for measuring leakages of information from a system to
an attacker, shows promising results as features for the task. In particular,
experiments with real-world datasets indicate that QIF is statistically tied to
the state of the art. Our initial results motivate further inquiries on how QIF
relates to causality and what are its limitations.
