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  On Causal Discovery with Cyclic Additive Noise Models

Mooij, J., Janzing, D., Heskes, T., & Schölkopf, B. (2012). On Causal Discovery with Cyclic Additive Noise Models. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, & K. Weinberger (Eds.), Advances in Neural Information Processing Systems 24 (pp. 639-647). Red Hook, NY, USA: Curran.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B87E-E Version Permalink: http://hdl.handle.net/21.11116/0000-0001-1968-F
Genre: Conference Paper

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 Creators:
Mooij, J, Author              
Janzing, D1, Author              
Heskes, T, Author
Schölkopf, B1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise. We prove that the causal graph of such models is generically identifiable in the bivariate, Gaussian-noise case. We also propose a method to learn such models from observational data. In the acyclic case, the method reduces to ordinary regression, but in the more challenging cyclic case, an additional term arises in the loss function, which makes it a special case of nonlinear independent component analysis. We illustrate the proposed method on synthetic data.

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 Dates: 2012-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: MooijJSH2012
 Degree: -

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Title: Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)
Place of Event: Granada, Spain
Start-/End Date: -

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Title: Advances in Neural Information Processing Systems 24
Source Genre: Proceedings
 Creator(s):
Shawe-Taylor, J, Editor
Zemel, RS, Editor
Bartlett, P, Editor
Pereira, F, Editor
Weinberger, KQ, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 639 - 647 Identifier: ISBN: 978-1-618-39599-3