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  Telling Cause from Effect in Deterministic Linear Dynamical Systems

Shajarisales, N., Janzing, D., Schölkopf, B., & Besserve, M. (2015). Telling Cause from Effect in Deterministic Linear Dynamical Systems. In F. Bach, & D. Blei (Eds.), International Conference on Machine Learning, 7-9 July 2015, Lille, France (pp. 285-294). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-4580-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-ACA1-7
Genre: Conference Paper

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 Creators:
Shajarisales, N, Author
Janzing, D1, Author              
Schölkopf, B1, Author              
Besserve, M2, 3, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Telling a cause from its effect using observed time series data is a major challenge in natural and social sciences. Assuming the effect is generated by the cause through a linear system, we propose a new approach based on the hypothesis that nature chooses the “cause” and the “mechanism generating the effect from the cause” independently of each other. Specifically we postulate that the power spectrum of the “cause” time series is uncorrelated with the square of the frequency response of the linear filter (system) generating the effect. While most causal discovery methods for time series mainly rely on the noise, our method relies on asymmetries of the power spectral density properties that exist even in deterministic systems. We describe mathematical assumptions in a deterministic model under which the causal direction is identifiable. In particular, we show a scenario where the method works but Granger causality fails. Experiments show encouraging results on synthetic as well as real-world data. Overall, this suggests that the postulate of Independence of Cause and Mechanism is a promising principle for causal inference on observed time series.

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 Dates: 2015-07
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: ShajarisalesJSB2015
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Title: 32nd International Conference on Machine Learning (ICML 2015)
Place of Event: Lille, France
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Title: International Conference on Machine Learning, 7-9 July 2015, Lille, France
Source Genre: Proceedings
 Creator(s):
Bach , F., Editor
Blei, D., Editor
Affiliations:
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Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 285 - 294 Identifier: -

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
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Pages: - Volume / Issue: 37 Sequence Number: - Start / End Page: - Identifier: -