PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Distinguishing between cause and effect
Joris Mooij and Dominik Janzing
Journal of Machine Learning Research Workshop & Conference Proceedings Volume 6, pp. 147-156, 2010. ISSN 1938-7228

Abstract

We describe eight data sets that together formed the CauseEffectPairs task in the Causality Challenge #2: Pot-Luck competition. Each set consists of a sample of a pair of statistically dependent random variables. One variable is known to cause the other one, but this information was hidden from the participants; the task was to identify which of the two variables was the cause and which one the effect, based upon the observed sample. The data sets were chosen such that we expect common agreement on the ground truth. 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 between the two variables in each pair. We also present baseline results using three different causal inference methods.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7848
Deposited By:Joris Mooij
Deposited On:17 March 2011