{"language":[{"iso":"eng"}],"title":"The third pillar of causal analysis? A measurement perspective on causal representations","month":"12","oa":1,"quality_controlled":"1","date_published":"2025-12-15T00:00:00Z","status":"public","acknowledgement":"This research was funded in whole or in part by the Austrian Science Fund (FWF) 10.55776/COE12. For open access purposes, the author has applied a CC BY public copyright license to any accepted manuscript version arising from this submission.\r\n","OA_place":"repository","abstract":[{"lang":"eng","text":"Causal reasoning and discovery, two fundamental tasks of causal analysis,\r\noften face challenges in applications due to the complexity, noisiness, and highdimensionality of real-world data. Despite recent progress in identifying latent\r\ncausal structures using causal representation learning (CRL), what makes learned\r\nrepresentations useful for causal downstream tasks and how to evaluate them are\r\nstill not well understood. In this paper, we reinterpret CRL using a measurement\r\nmodel framework, where the learned representations are viewed as proxy measurements of the latent causal variables. Our approach clarifies the conditions under\r\nwhich learned representations support downstream causal reasoning and provides\r\na principled basis for quantitatively assessing the quality of representations using\r\na new Test-based Measurement EXclusivity (T-MEX) score. We validate T-MEX\r\nacross diverse causal inference scenarios, including numerical simulations and\r\nreal-world ecological video analysis, demonstrating that the proposed framework\r\nand corresponding score effectively assess the identification of learned representations and their usefulness for causal downstream tasks. Reproducible code can\r\nbe found at https://github.com/shimenghuang/a-measurement-perspective-of-crl."}],"OA_type":"green","citation":{"mla":"Yao, Dingling, et al. “The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations.” <i>39th Annual Conference on Neural Information Processing Systems</i>, vol. 38, Neural Information Processing Systems Foundation, 2025.","ama":"Yao D, Huang S, Cadei R, Zhang K, Locatello F. The third pillar of causal analysis? A measurement perspective on causal representations. In: <i>39th Annual Conference on Neural Information Processing Systems</i>. Vol 38. Neural Information Processing Systems Foundation; 2025.","short":"D. Yao, S. Huang, R. Cadei, K. Zhang, F. Locatello, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.","chicago":"Yao, Dingling, Shimeng Huang, Riccardo Cadei, Kun Zhang, and Francesco Locatello. “The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations.” In <i>39th Annual Conference on Neural Information Processing Systems</i>, Vol. 38. Neural Information Processing Systems Foundation, 2025.","apa":"Yao, D., Huang, S., Cadei, R., Zhang, K., &#38; Locatello, F. (2025). The third pillar of causal analysis? A measurement perspective on causal representations. In <i>39th Annual Conference on Neural Information Processing Systems</i> (Vol. 38). San Diego, CA, United States: Neural Information Processing Systems Foundation.","ista":"Yao D, Huang S, Cadei R, Zhang K, Locatello F. 2025. The third pillar of causal analysis? A measurement perspective on causal representations. 39th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 38.","ieee":"D. Yao, S. Huang, R. Cadei, K. Zhang, and F. Locatello, “The third pillar of causal analysis? A measurement perspective on causal representations,” in <i>39th Annual Conference on Neural Information Processing Systems</i>, San Diego, CA, United States, 2025, vol. 38."},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2505.17708"}],"ddc":["000"],"arxiv":1,"_id":"21068","has_accepted_license":"1","year":"2025","publisher":"Neural Information Processing Systems Foundation","corr_author":"1","oa_version":"Preprint","alternative_title":["Advances in Neural Information Processing Systems"],"external_id":{"arxiv":["2505.17708"]},"conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2025-12-07","location":"San Diego, CA, United States","start_date":"2025-12-02"},"date_created":"2026-01-29T14:24:56Z","date_updated":"2026-02-10T12:08:52Z","volume":38,"department":[{"_id":"FrLo"}],"publication_status":"epub_ahead","publication_identifier":{"issn":["1049-5258"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":"        38","day":"15","author":[{"id":"d3e02e50-48a8-11ee-8f62-c108061797fa","first_name":"Dingling","last_name":"Yao","full_name":"Yao, Dingling"},{"id":"989c2a06-fb4e-11ef-a992-ab766442255b","orcid":"0000-0001-6919-821X","first_name":"Shimeng","last_name":"Huang","full_name":"Huang, Shimeng"},{"full_name":"Cadei, Riccardo","id":"0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b","first_name":"Riccardo","last_name":"Cadei"},{"full_name":"Zhang, Kun","first_name":"Kun","last_name":"Zhang"},{"last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"}],"type":"conference","related_material":{"link":[{"url":"https://github.com/shimenghuang/a-measurement-perspective-of-crl","relation":"software"}]},"publication":"39th Annual Conference on Neural Information Processing Systems","article_processing_charge":"No"}