Nonlinear causal discovery with additive noise models

Hoyer, PO; Janzing, D; Mooij, JM; Peters, J; Schölkopf, B

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Nonlinear causal discovery with additive noise models

Hoyer, P., Janzing, D., Mooij, J., Peters, J., & Schölkopf, B. (2009). Nonlinear causal discovery with additive noise models. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 689-696). Red Hook, NY, USA: Curran.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C499-F Version Permalink: https://hdl.handle.net/21.11116/0000-0002-DE55-5

Genre: Conference Paper

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https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf (Publisher version) Open Access status unknown

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Creators:
Hoyer, PO, Author
Janzing, D^{1, 2}, Author
Mooij, JM^{1, 2}, Author
Peters, J^{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: The discovery of causal relationships between a set of observed variables is a fundamental problem in science. For continuous-valued data linear acyclic causal models are often used because these models are well understood and there are well-known methods to fit them to data. In reality, of course, many causal relationships are more or less nonlinear, raising some doubts as to the applicability and usefulness of purely linear methods. In this contribution we show that in fact the basic linear framework can be generalized to nonlinear models with additive noise. In this extended framework, nonlinearities in the data-generating process are in fact a blessing rather than a curse, as they typically provide information on the underlying causal system and allow more aspects of the true data-generating mechanisms to be identified. In addition to theoretical results we show simulations and some simple real data experiments illustrating the identification power provided by nonlinearities.

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Dates: Date issued: 2009-06

Publication Status: Issued

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Identifiers: BibTex Citekey: 5406

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Title: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)

Place of Event: Vancouver, BC, Canada

Start-/End Date: 2008-12-08 - 2008-12-10

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Title: Advances in neural information processing systems 21

Source Genre: Proceedings

Creator(s):
Koller, D, Editor
Schuurmans, D, Editor
Bengio, Y, Editor
Bottou, L, Editor

Affiliations:
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Publ. Info: Red Hook, NY, USA : Curran

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 689 - 696 Identifier: ISBN: 978-1-60560-949-2