Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
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.
Cite
Text
Rolland et al. "Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models." International Conference on Machine Learning, 2022.Markdown
[Rolland et al. "Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models." International Conference on Machine Learning, 2022.](https://mlanthology.org/icml/2022/rolland2022icml-score/)BibTeX
@inproceedings{rolland2022icml-score,
title = {{Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models}},
author = {Rolland, Paul and Cevher, Volkan and Kleindessner, Matthäus and Russell, Chris and Janzing, Dominik and Schölkopf, Bernhard and Locatello, Francesco},
booktitle = {International Conference on Machine Learning},
year = {2022},
pages = {18741-18753},
volume = {162},
url = {https://mlanthology.org/icml/2022/rolland2022icml-score/}
}