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Results of the Cause-Effect Pair Challenge

  • Isabelle Guyon
  • Alexander Statnikov
Chapter
Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)

Abstract

We organized a challenge in causal discovery from observational data with the aim of devising a “causation coefficient” to score pairs of variables. The participants were provided with a large database of thousands of pairs of variables {X, Y } (80% semi-artificial data and 20% real data) from which samples were drawn independently (i.e. ignoring possible time dependencies). The goal was to discover whether the data supports the hypothesis that Y = f(X, noise), which for the purpose of this challenge was our definition of causality (X causes Y). The participants adopted a machine learning approach, which contrasts with previously published model-based methods. They extracted numerous features of the joint empirical distribution of X and Y and built a classifier to separate pairs belonging to the class “X causes Y” from other cases (“Y causes X”, “X and Y are related” but not in a causal way, a third variable may be causing both X and Y, “X and Y are independent”). The classifier was trained from examples provided by the organizers and tested on independent test data for which the truth values of causal relationships was known only to the organizers. The participants achieved an Area under the ROC Curve (AUC) over 0.8 in the first phase deployed on the Kaggle challenge, which ran from March through September 2013 (round 1). The participants were then invited to improve upon the code efficiency by submitting fast causation coefficients on the Codalab platform (round 2). The causation coefficients developed by the winners have been made available under open source licenses. We have made all data and code publicly available at http://www.causality.inf.ethz.ch/CEdata/.

Keywords

Causal discovery Cause-effect pairs Benchmark Challenge 

Notes

Acknowledgements

We are very grateful to all those who contributed time to make this challenge happen. The initial impulse for this challenge was given by Joris Mooij, Dominik Janzing, and Bernhard Schoelkopf, from the Max Planck Institute who devised a cause-effect pair task, which was part of the NIPS 2008 Pot-Luck challenge http://www.causality.inf.ethz.ch/pot-luck.php. Examples of algorithms and data were supplied by Povilas Daniusis, Arthur Gretton, Patrik O. Hoyer, Dominik Janzing, Antti Kerminen, Joris Mooij, Jonas Peters, Bernhard Schoekopf, Shohei Shimizu, Oliver Stegle, and Kun Zhang, and Jakob Zscheischler. The datasets were prepared by Isabelle Guyon, Mehreen Saeed, Mikael Henaff, Sisi Ma, and Alexander Statnikov. The website and the sample code were prepared by Isabelle Guyon and Ben Hamner. The challenge protocol and implementation was tested and/or reviewed by Marc Boullé, Léon Bottou, Hugo Jair Escalante, Frederick Eberhardt, Seth Flaxman, Patrik Hoyer, Dominik Janzing, Richard Kennaway, Vincent Lemaire, Joris Mooij, Jonas Peters, Florin, Peter Spirtes, Ioannis Tsamardinos, Jianxin Yin,and Kun Zhang. The second round was supported by Microsoft Research. We are very grateful to Evelyne Viegas for her help and advice.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Isabelle Guyon
    • 1
    • 2
  • Alexander Statnikov
    • 3
  1. 1.Team TAU - CNRS, INRIA, Université Paris SudUniversité Paris SaclayOrsayFrance
  2. 2.ChaLearnBerkeleyUSA
  3. 3.SoFiSan FranciscoUSA

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