The goal of this task is to distinguish between cause and effect.
For this task, the origin of the data is hidden for the participants but known to the organizers. The data sets are chosen such that we expect common agreement on which one is the cause and which one the effect. Even though part of the statistical dependences may also be due to hidden common causes, common sense tells us that there is a significant cause-effect-relation.
The data set contains 8 different matrices, in plain text format.
Because the competition is over, the ground truths and description of the datasets are now available.
This task was contributed by Joris Mooij, Dominik Janzing and Bernhard Schölkopf.
After the challenge, we have started collecting more datasets for benchmark purposes. This is still work in progress, and in particular, we do not guarantee that the provided ground truths are correct. If you have any comments, questions, or suggestions for additional data sets, please contact Jakob Zscheischler.