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  A Kernel-Based Causal Learning Algorithm

Sun, X., Janzing, D., Schölkopf, B., & Fukumizu, K. (2007). A Kernel-Based Causal Learning Algorithm. In Z. Ghahramani (Ed.), ICML '07: 24th International Conference on Machine Learning (pp. 855-862). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CD57-8 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-E23E-9
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1273496.1273604 (Publisher version)
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 Creators:
Sun, X1, 2, Author           
Janzing, D, Author           
Schölkopf, B1, 2, Author           
Fukumizu, K, 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: We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the line of the common faithfulness assumption of constraint-based causal learning, our approach assumes that a variable Z is likely to be a common effect of X and Y, if conditioning on Z increases the dependence between X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges by the majority principle. In most experiments with known causal structures, our method provided plausible results and outperformed the conventional constraint-based PC algorithm.

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 Dates: 2007-06
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1145/1273496.1273604
BibTex Citekey: 4457
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Title: 24th Annual International Conference on Machine Learning (ICML 2007)
Place of Event: Corvallis, OR, USA
Start-/End Date: 2007-06-20 - 2007-06-24

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Title: ICML '07: 24th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Ghahramani, Z, Editor
Affiliations:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 855 - 862 Identifier: ISBN: 978-1-59593-793-3