Conditional distribution variability measures for causality detection

In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the the framework of the third Chalearn causality challenge. The developed model obtains an AUC score of 0.81 (team Jarfo) on the final test database and ranked second in the cause-effect pairs challenge.