Score matching enables causal discovery of nonlinear additive noise models
Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello F. 2022. Score matching enables causal discovery of nonlinear additive noise models. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 162, 18741–18753.
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Conference Paper
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Author
Rolland, Paul;
Cevher, Volkan;
Kleindessner, Matthäus;
Russel, Chris;
Schölkopf, Bernhard;
Janzing, Dominik;
Locatello, FrancescoISTA 
Department
Series Title
PMLR
Abstract
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-the-art causal discovery methods while
being significantly faster.
Publishing Year
Date Published
2022-07-22
Proceedings Title
Proceedings of the 39th International Conference on Machine Learning
Publisher
ML Research Press
Volume
162
Page
18741-18753
Conference
International Conference on Machine Learning
Conference Location
Baltimore, MD, United States
Conference Date
2022-07-17 – 2022-07-23
IST-REx-ID
Cite this
Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal discovery of nonlinear additive noise models. In: Proceedings of the 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:18741-18753.
Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing, D., & Locatello, F. (2022). Score matching enables causal discovery of nonlinear additive noise models. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 18741–18753). Baltimore, MD, United States: ML Research Press.
Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models.” In Proceedings of the 39th International Conference on Machine Learning, 162:18741–53. ML Research Press, 2022.
P. Rolland et al., “Score matching enables causal discovery of nonlinear additive noise models,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753.
Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello F. 2022. Score matching enables causal discovery of nonlinear additive noise models. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 162, 18741–18753.
Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 18741–53.
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