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  4. Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
 
conference paper

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

Rolland, Paul Thierry Yves  
•
Cevher, Volkan  orcid-logo
•
Kleindessner, Matthäus
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2022
International Conference On Machine Learning
38th International Conference on Machine Learning (ICML)

This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal discovery methods. To showcase our approach, we also propose a new efficient method for approximating the score’s Jacobian, enabling to recover the causal graph. Empirically, we find that the new algorithm, called SCORE, is competitive with state-of-theart causal discovery methods while being significantly faster.

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ICML 2022.pdf

Type

Postprint

Version

http://purl.org/coar/version/c_ab4af688f83e57aa

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openaccess

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copyright

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295.44 KB

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Adobe PDF

Checksum (MD5)

7300ad5a270ac69bd39fae1c67224929

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