{"scopus_import":"1","status":"public","related_material":{"link":[{"url":"https://github.com/CausalLearningAI/crl-dynamical-systems","relation":"software"}]},"external_id":{"arxiv":["2405.13888"]},"title":"Marrying causal representation learning with dynamical systems for science","acknowledgement":"We thank Niklas Boers for recommending the SpeedyWeather simulator and Valentino Maiorca\r\nfor guidance on Fourier transformation for SST data. We are also grateful to Shimeng Huang and Riccardo Cadei for their feedback on the treatment effect estimation experiment and to Jiale Chen and Adeel Pervez for their assistance with the solver implementation. Finally, we appreciate the anonymous reviewers for their insightful suggestions, which helped improve the manuscript. ","oa_version":"Published Version","quality_controlled":"1","publication_status":"published","publisher":"Neural Information Processing Systems Foundation","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","alternative_title":["Advances in Neural Information Processing Systems"],"department":[{"_id":"CaMu"},{"_id":"FrLo"}],"publication":"38th Conference on Neural Information Processing Systems","date_published":"2024-12-01T00:00:00Z","abstract":[{"text":"Causal representation learning promises to extend causal models to hidden causal\r\nvariables from raw entangled measurements. However, most progress has focused\r\non proving identifiability results in different settings, and we are not aware of any\r\nsuccessful real-world application. At the same time, the field of dynamical systems\r\nbenefited from deep learning and scaled to countless applications but does not allow\r\nparameter identification. In this paper, we draw a clear connection between the two\r\nand their key assumptions, allowing us to apply identifiable methods developed\r\nin causal representation learning to dynamical systems. At the same time, we can\r\nleverage scalable differentiable solvers developed for differential equations to build\r\nmodels that are both identifiable and practical. Overall, we learn explicitly controllable models that isolate the trajectory-specific parameters for further downstream\r\ntasks such as out-of-distribution classification or treatment effect estimation. We\r\nexperiment with a wind simulator with partially known factors of variation. We\r\nalso apply the resulting model to real-world climate data and successfully answer\r\ndownstream causal questions in line with existing literature on climate change.\r\nCode is available at https://github.com/CausalLearningAI/crl-dynamical-systems.","lang":"eng"}],"arxiv":1,"OA_place":"publisher","day":"01","article_processing_charge":"No","type":"conference","year":"2024","license":"https://creativecommons.org/licenses/by/4.0/","has_accepted_license":"1","date_created":"2025-02-05T07:49:00Z","conference":{"start_date":"2024-12-16","location":"Vancouver, Canada","name":"NeurIPS: Neural Information Processing Systems","end_date":"2024-12-16"},"file":[{"access_level":"open_access","creator":"dernst","file_id":"19006","content_type":"application/pdf","date_updated":"2025-02-05T07:44:58Z","checksum":"fe8832367e7143876f178244385d859e","success":1,"date_created":"2025-02-05T07:44:58Z","file_size":2595855,"relation":"main_file","file_name":"2024_NeurIPS_Yao.pdf"}],"OA_type":"gold","ddc":["000","550"],"date_updated":"2025-07-10T11:51:32Z","language":[{"iso":"eng"}],"tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"volume":37,"corr_author":"1","citation":{"chicago":"Yao, Dingling, Caroline J Muller, and Francesco Locatello. “Marrying Causal Representation Learning with Dynamical Systems for Science.” In <i>38th Conference on Neural Information Processing Systems</i>, Vol. 37. Neural Information Processing Systems Foundation, 2024.","ista":"Yao D, Muller CJ, Locatello F. 2024. Marrying causal representation learning with dynamical systems for science. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.","ieee":"D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning with dynamical systems for science,” in <i>38th Conference on Neural Information Processing Systems</i>, Vancouver, Canada, 2024, vol. 37.","short":"D. Yao, C.J. Muller, F. Locatello, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.","ama":"Yao D, Muller CJ, Locatello F. Marrying causal representation learning with dynamical systems for science. In: <i>38th Conference on Neural Information Processing Systems</i>. Vol 37. Neural Information Processing Systems Foundation; 2024.","mla":"Yao, Dingling, et al. “Marrying Causal Representation Learning with Dynamical Systems for Science.” <i>38th Conference on Neural Information Processing Systems</i>, vol. 37, Neural Information Processing Systems Foundation, 2024.","apa":"Yao, D., Muller, C. J., &#38; Locatello, F. (2024). Marrying causal representation learning with dynamical systems for science. In <i>38th Conference on Neural Information Processing Systems</i> (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation."},"file_date_updated":"2025-02-05T07:44:58Z","oa":1,"_id":"19005","author":[{"last_name":"Yao","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling","first_name":"Dingling"},{"last_name":"Muller","id":"f978ccb0-3f7f-11eb-b193-b0e2bd13182b","first_name":"Caroline J","orcid":"0000-0001-5836-5350","full_name":"Muller, Caroline J"},{"orcid":"0000-0002-4850-0683","first_name":"Francesco","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello"}],"intvolume":"        37","month":"12"}