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  Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective

Zhang, K., & Hyvärinen, A. (2009). Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. In W. Buntine, D. Mladenić, & J. Shawe-Taylor (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009 (pp. 570-585). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C303-9 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-E525-2
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F978-3-642-04174-7_37.pdf (Publisher version)
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 Creators:
Zhang, K1, Author           
Hyvärinen, A, Author
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1External Organizations, ou_persistent22              

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 Abstract: We consider causally sufficient acyclic causal models in which the relationship among the variables is nonlinear while disturbances have linear effects, and show that three principles, namely, the causal Markov condition (together with the independence between each disturbance and the corresponding parents), minimum disturbance entropy, and mutual independence of the disturbances, are equivalent. This motivates new and more efficient methods for some causal discovery problems. In particular, we propose to use multichannel blind deconvolution, an extension of independent component analysis, to do Granger causality analysis with instantaneous effects. This approach gives more accurate estimates of the parameters and can easily incorporate sparsity constraints. For additive disturbance-based nonlinear causal discovery, we first make use of the conditional independence relationships to obtain the equivalence class; undetermined causal directions are then found by nonlinear regression and pairwise independence tests. This avoids the brute-force search and greatly reduces the computational load.

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 Dates: 2009-09
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-04174-7_37
BibTex Citekey: ZhangH2009
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Title: 16th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2009)
Place of Event: Bled, Slovenia
Start-/End Date: 2009-09-07 - 2009-09-11

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Title: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009
Source Genre: Proceedings
 Creator(s):
Buntine, W, Editor
Grobelnik, M, Author
Mladenić, D, Editor
Shawe-Taylor, J, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 570 - 585 Identifier: ISBN: 978-3-642-04174-7

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Title: Lecture Notes in Computer Science
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Pages: - Volume / Issue: 5781 Sequence Number: - Start / End Page: - Identifier: -