{"month":"07","alternative_title":["PMLR"],"page":"18741-18753","publisher":"ML Research Press","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2203.04413"}],"_id":"14171","quality_controlled":"1","oa_version":"Preprint","external_id":{"arxiv":["2203.04413"]},"intvolume":"       162","article_processing_charge":"No","date_created":"2023-08-22T14:00:18Z","publication":"Proceedings of the 39th International Conference on Machine Learning","type":"conference","abstract":[{"text":"This paper demonstrates how to recover causal graphs from the score of the\r\ndata distribution in non-linear additive (Gaussian) noise models. Using score\r\nmatching algorithms as a building block, we show how to design a new generation\r\nof scalable causal discovery methods. To showcase our approach, we also propose\r\na new efficient method for approximating the score's Jacobian, enabling to\r\nrecover the causal graph. Empirically, we find that the new algorithm, called\r\nSCORE, is competitive with state-of-the-art causal discovery methods while\r\nbeing significantly faster.","lang":"eng"}],"volume":162,"oa":1,"author":[{"full_name":"Rolland, Paul","first_name":"Paul","last_name":"Rolland"},{"last_name":"Cevher","first_name":"Volkan","full_name":"Cevher, Volkan"},{"last_name":"Kleindessner","first_name":"Matthäus","full_name":"Kleindessner, Matthäus"},{"first_name":"Chris","full_name":"Russel, Chris","last_name":"Russel"},{"first_name":"Bernhard","full_name":"Schölkopf, Bernhard","last_name":"Schölkopf"},{"full_name":"Janzing, Dominik","first_name":"Dominik","last_name":"Janzing"},{"orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"extern":"1","date_published":"2022-07-22T00:00:00Z","day":"22","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Score matching enables causal discovery of nonlinear additive noise  models","year":"2022","publication_status":"published","status":"public","date_updated":"2023-09-11T10:14:20Z","language":[{"iso":"eng"}],"conference":{"location":"Baltimore, MD, United States","end_date":"2022-07-23","name":"International Conference on Machine Learning","start_date":"2022-07-17"},"department":[{"_id":"FrLo"}],"citation":{"apa":"Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing, D., &#38; Locatello, F. (2022). Score matching enables causal discovery of nonlinear additive noise  models. In <i>Proceedings of the 39th International Conference on Machine Learning</i> (Vol. 162, pp. 18741–18753). Baltimore, MD, United States: ML Research Press.","ista":"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.","ieee":"P. Rolland <i>et al.</i>, “Score matching enables causal discovery of nonlinear additive noise  models,” in <i>Proceedings of the 39th International Conference on Machine Learning</i>, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753.","chicago":"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 <i>Proceedings of the 39th International Conference on Machine Learning</i>, 162:18741–53. ML Research Press, 2022.","short":"P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 18741–18753.","ama":"Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal discovery of nonlinear additive noise  models. In: <i>Proceedings of the 39th International Conference on Machine Learning</i>. Vol 162. ML Research Press; 2022:18741-18753.","mla":"Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” <i>Proceedings of the 39th International Conference on Machine Learning</i>, vol. 162, ML Research Press, 2022, pp. 18741–53."}}