  We are interested in learning causal relationships between pairs of random
variables, purely from observational data. To effectively address this task,
the state-of-the-art relies on strong assumptions regarding the mechanisms
mapping causes to effects, such as invertibility or the existence of additive
noise, which only hold in limited situations. On the contrary, this short paper
proposes to learn how to perform causal inference directly from data, and
without the need of feature engineering. In particular, we pose causality as a
kernel mean embedding classification problem, where inputs are samples from
arbitrary probability distributions on pairs of random variables, and labels
are types of causal relationships. We validate the performance of our method on
synthetic and real-world data against the state-of-the-art. Moreover, we
submitted our algorithm to the ChaLearn's "Fast Causation Coefficient
Challenge" competition, with which we won the fastest code prize and ranked
third in the overall leaderboard.
