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conference paper
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
2022
International Conference On Machine Learning
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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Name
ICML 2022.pdf
Type
postprint
Access type
openaccess
License Condition
copyright
Size
295.44 KB
Format
Adobe PDF
Checksum (MD5)
7300ad5a270ac69bd39fae1c67224929