{"issue":"5","publication":"Proceedings of the IEEE","publisher":"Institute of Electrical and Electronics Engineers","external_id":{"arxiv":["2102.11107"]},"month":"05","date_created":"2023-08-21T12:19:30Z","publication_status":"published","extern":"1","_id":"14117","status":"public","keyword":["Electrical and Electronic Engineering"],"article_type":"original","department":[{"_id":"FrLo"}],"doi":"10.1109/jproc.2021.3058954","oa_version":"Published Version","date_updated":"2023-09-11T11:43:35Z","type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1109/JPROC.2021.3058954"}],"page":"612-634","author":[{"full_name":"Scholkopf, Bernhard","first_name":"Bernhard","last_name":"Scholkopf"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello"},{"first_name":"Stefan","last_name":"Bauer","full_name":"Bauer, Stefan"},{"full_name":"Ke, Nan Rosemary","last_name":"Ke","first_name":"Nan Rosemary"},{"first_name":"Nal","last_name":"Kalchbrenner","full_name":"Kalchbrenner, Nal"},{"first_name":"Anirudh","last_name":"Goyal","full_name":"Goyal, Anirudh"},{"last_name":"Bengio","first_name":"Yoshua","full_name":"Bengio, Yoshua"}],"language":[{"iso":"eng"}],"day":"01","oa":1,"abstract":[{"lang":"eng","text":"The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities."}],"year":"2021","scopus_import":"1","volume":109,"date_published":"2021-05-01T00:00:00Z","quality_controlled":"1","citation":{"ieee":"B. Scholkopf <i>et al.</i>, “Toward causal representation learning,” <i>Proceedings of the IEEE</i>, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021.","short":"B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.","chicago":"Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>.","ama":"Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning. <i>Proceedings of the IEEE</i>. 2021;109(5):612-634. doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>","apa":"Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., &#38; Bengio, Y. (2021). Toward causal representation learning. <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>","mla":"Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>.","ista":"Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5), 612–634."},"title":"Toward causal representation learning","intvolume":"       109","article_processing_charge":"No","publication_identifier":{"eissn":["1558-2256"],"issn":["0018-9219"]}}