[te]

Interestingly, the authors do acknowledge on p. 46 that their sample size is too small to obtain a statistically significant result:

A rough estimate how large the CauseEffectPairs benchmark should have been in order to obtain significant results can easily be made. Using a standard (conservative) Bon- ferroni correction, taking into account that we compared 37 methods, we would need about 120 (weighted) pairs for an accuracy of 65% to be considered significant (with two-sided testing and 5% significance threshold). This is about four times as much as the current number of 37 (weighted) pairs in the CauseEffectPairs benchmark. Therefore, we sug- gest that at this point, the highest priority regarding future work should be to obtain more validation data, rather than developing additional methods or optimizing computation time of existing methods. We hope that our publication of the CauseEffectPairs benchmark data inspires researchers to collaborate on this important task and we invite everybody to contribute pairs to the CauseEffectPairs benchmark data.

[aab0]

If you want to distinguish a particular method, but you can definitely tell that overall, the methods are collectively outperforming chance and so in this dataset, it is possible to infer the direction of causation.