Detecting the Direction of Causal Time Series

Peters, J; Janzing, D; Gretton, A; Schölkopf, B

doi:10.1145/1553374.1553477

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Detecting the Direction of Causal Time Series

Peters, J., Janzing, D., Gretton, A., & Schölkopf, B. (2009). Detecting the Direction of Causal Time Series. In A. Danyluk, L. Bottou, & M. Danyluk (Eds.), ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning (pp. 801-808). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C477-C Version Permalink: https://hdl.handle.net/21.11116/0000-0002-F922-F

Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1553374.1553477 (Publisher version) Open Access status unknown

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Creators:
Peters, J^{1, 2}, Author
Janzing, D^{1, 2}, Author
Gretton, A^{1, 2}, Author
Schölkopf, B^{1, 2}, Author

Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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Abstract: We propose a method that detects the true
direction of time series, by fitting an autoregressive
moving average model to the data.
Whenever the noise is independent of the previous
samples for one ordering of the observations,
but dependent for the opposite ordering,
we infer the former direction to be the
true one. We prove that our method works
in the population case as long as the noise of
the process is not normally distributed (for
the latter case, the direction is not identificable).
A new and important implication of
our result is that it confirms a fundamental
conjecture in causal reasoning - if after regression
the noise is independent of signal for
one direction and dependent for the other,
then the former represents the true causal
direction - in the case of time series. We
test our approach on two types of data: simulated
data sets conforming to our modeling
assumptions, and real world EEG time series.
Our method makes a decision for a significant
fraction of both data sets, and these
decisions are mostly correct. For real world
data, our approach outperforms alternative
solutions to the problem of time direction recovery.

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Dates: Date issued: 2009-06

Publication Status: Issued

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Identifiers: DOI: 10.1145/1553374.1553477
BibTex Citekey: 5902

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Title: 26th International Conference on Machine Learning

Place of Event: Montreal, Canada

Start-/End Date: 2009-06-14 - 2009-06-18

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Title: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

Source Genre: Proceedings

Creator(s):
Danyluk, A, Editor
Bottou, L, Editor
Danyluk, M, Editor

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Publ. Info: New York, NY, USA : ACM Press

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 801 - 808 Identifier: ISBN: 978-1-60558-516-1

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Title: ACM International Conference Proceeding Series

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Pages: - Volume / Issue: 382 Sequence Number: - Start / End Page: - Identifier: -