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  Testing whether linear equations are causal: A free probability theory approach

Zscheischler, J., Janzing, D., & Zhang, K. (2011). Testing whether linear equations are causal: A free probability theory approach. In F. Cozman, & A. Pfeffer (Eds.), 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 839-847). Corvallis, OR, USA: AUAI Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BB32-B Version Permalink: http://hdl.handle.net/21.11116/0000-0002-0AA1-D
Genre: Conference Paper

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http://www.auai.org/uai2011/ (Table of contents)
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 Creators:
Zscheischler, J1, Author              
Janzing, D1, Author              
Zhang, K1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: We propose a method that infers whether linear relations between two high-dimensional variables X and Y are due to a causal influence from X to Y or from Y to X. The earlier proposed so-called Trace Method is extended to the regime where the dimension of the observed variables exceeds the sample size. Based on previous work, we postulate conditions that characterize a causal relation between X and Y . Moreover, we describe a statistical test and argue that both causal directions are typically rejected if there is a common cause. A full theoretical analysis is presented for the deterministic case but our approach seems to be valid for the noisy case, too, for which we additionally present an approach based on a sparsity constraint. The discussed method yields promising results for both simulated and real world data.

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 Dates: 2011-07
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: ZscheischlerJZ2011
 Degree: -

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Title: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Place of Event: Barcelona, Spain
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Title: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Source Genre: Proceedings
 Creator(s):
Cozman, FG, Editor
Pfeffer, A, Editor
Affiliations:
-
Publ. Info: Corvallis, OR, USA : AUAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 839 - 847 Identifier: ISBN: 978-0-9749039-7-2