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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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 Creators:
Peters, J1, 2, Author           
Janzing, D1, 2, Author           
Gretton, A1, 2, Author           
Schölkopf, B1, 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: 2009-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1553374.1553477
BibTex Citekey: 5902
 Degree: -

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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
Affiliations:
-
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
Source Genre: Series
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Pages: - Volume / Issue: 382 Sequence Number: - Start / End Page: - Identifier: -