Distinguishing between cause and effect

Joris Mooij, Dominik Janzing
; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:147-156, 2010.

Abstract

We describe eight data sets that together formed the \textttCauseEffectPairs task in the \emphCausality 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v6-mooij10a, title = {Distinguishing between cause and effect}, author = {Joris Mooij and Dominik Janzing}, pages = {147--156}, year = {2010}, editor = {Isabelle Guyon and Dominik Janzing and Bernhard Schölkopf}, volume = {6}, series = {Proceedings of Machine Learning Research}, address = {Whistler, Canada}, month = {12 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v6/mooij10a/mooij10a.pdf}, url = {http://proceedings.mlr.press/v6/mooij10a.html}, abstract = {We describe eight data sets that together formed the \textttCauseEffectPairs task in the \emphCausality 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.} }
Endnote
%0 Conference Paper %T Distinguishing between cause and effect %A Joris Mooij %A Dominik Janzing %B Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 %C Proceedings of Machine Learning Research %D 2010 %E Isabelle Guyon %E Dominik Janzing %E Bernhard Schölkopf %F pmlr-v6-mooij10a %I PMLR %J Proceedings of Machine Learning Research %P 147--156 %U http://proceedings.mlr.press %V 6 %W PMLR %X We describe eight data sets that together formed the \textttCauseEffectPairs task in the \emphCausality 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.
RIS
TY - CPAPER TI - Distinguishing between cause and effect AU - Joris Mooij AU - Dominik Janzing BT - Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 PY - 2010/02/18 DA - 2010/02/18 ED - Isabelle Guyon ED - Dominik Janzing ED - Bernhard Schölkopf ID - pmlr-v6-mooij10a PB - PMLR SP - 147 DP - PMLR EP - 156 L1 - http://proceedings.mlr.press/v6/mooij10a/mooij10a.pdf UR - http://proceedings.mlr.press/v6/mooij10a.html AB - We describe eight data sets that together formed the \textttCauseEffectPairs task in the \emphCausality 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. ER -
APA
Mooij, J. & Janzing, D.. (2010). Distinguishing between cause and effect. Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, in PMLR 6:147-156

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