[{"conference":{"end_date":"2025-04-28","location":"Singapore","start_date":"2025-04-24","name":"ICLR: International Conference on Learning Representations"},"year":"2025","type":"conference","date_created":"2025-02-05T09:23:25Z","file":[{"relation":"main_file","creator":"flocatel","content_type":"application/pdf","date_created":"2026-01-27T12:43:25Z","date_updated":"2026-01-27T12:43:25Z","success":1,"file_name":"4356_Unifying_Causal_Represent (1).pdf","file_size":877014,"file_id":"21048","checksum":"c4b5a4a644228c6d1b0283e1368bce9e","access_level":"open_access"}],"publication":"13th International Conference on Learning Representations","publication_status":"published","date_updated":"2026-02-09T05:52:14Z","OA_place":"publisher","date_published":"2025-01-22T00:00:00Z","ddc":["000"],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","oa_version":"Published Version","has_accepted_license":"1","corr_author":"1","month":"01","oa":1,"license":"https://creativecommons.org/licenses/by/4.0/","title":"Unifying causal representation learning with the invariance principle","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"ICLR","_id":"19010","scopus_import":"1","department":[{"_id":"FrLo"}],"acknowledgement":"We thank Jiaqi Zhang, Francesco Montagna, David Lopez-Paz, Kartik Ahuja, Thomas Kipf, Sara\r\nMagliacane, Julius von Kügelgen, Kun Zhang, and Bernhard Schölkopf for extremely helpful discussion. Riccardo Cadei was supported by a Google Research Scholar Award to Francesco Locatello. We acknowledge the Third Bellairs Workshop on Causal Representation Learning held at the Bellairs Research Institute, February 9/16, 2024, and a debate on the difference between interventions and counterfactuals in disentanglement and CRL that took place during Dhanya Sridhar’s lecture, which motivated us to significantly broaden the scope of the paper. We thank Dhanya and all participants of the workshop.","citation":{"mla":"Yao, Dingling, et al. “Unifying Causal Representation Learning with the Invariance Principle.” <i>13th International Conference on Learning Representations</i>, ICLR, 2025.","ieee":"D. Yao, D. Rancati, R. Cadei, M. Fumero, and F. Locatello, “Unifying causal representation learning with the invariance principle,” in <i>13th International Conference on Learning Representations</i>, Singapore, 2025.","ama":"Yao D, Rancati D, Cadei R, Fumero M, Locatello F. Unifying causal representation learning with the invariance principle. In: <i>13th International Conference on Learning Representations</i>. ICLR; 2025.","apa":"Yao, D., Rancati, D., Cadei, R., Fumero, M., &#38; Locatello, F. (2025). Unifying causal representation learning with the invariance principle. In <i>13th International Conference on Learning Representations</i>. Singapore: ICLR.","short":"D. Yao, D. Rancati, R. Cadei, M. Fumero, F. Locatello, in:, 13th International Conference on Learning Representations, ICLR, 2025.","ista":"Yao D, Rancati D, Cadei R, Fumero M, Locatello F. 2025. Unifying causal representation learning with the invariance principle. 13th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","chicago":"Yao, Dingling, Dario Rancati, Riccardo Cadei, Marco Fumero, and Francesco Locatello. “Unifying Causal Representation Learning with the Invariance Principle.” In <i>13th International Conference on Learning Representations</i>. ICLR, 2025."},"status":"public","author":[{"last_name":"Yao","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling","first_name":"Dingling"},{"first_name":"Dario","last_name":"Rancati","id":"feb58f2e-72ef-11ef-b75a-8f0894539cd0","full_name":"Rancati, Dario"},{"first_name":"Riccardo","full_name":"Cadei, Riccardo","id":"0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b","last_name":"Cadei"},{"id":"1c1593eb-393f-11ef-bb8e-ab4f1e979650","full_name":"Fumero, Marco","last_name":"Fumero","first_name":"Marco"},{"first_name":"Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"language":[{"iso":"eng"}],"day":"22","file_date_updated":"2026-01-27T12:43:25Z","arxiv":1,"OA_type":"gold","external_id":{"arxiv":["2409.02772"]},"abstract":[{"text":"Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification. A plethora of methods have been developed, each tackling carefully crafted problem settings that lead to different types of identifiability. The folklore is that these different settings are important, as they are often linked to different rungs of Pearl's causal hierarchy, although not all neatly fit. Our main contribution is to show that many existing causal representation learning approaches methodologically align the representation to known data symmetries. Identification of the variables is guided by equivalence classes across different \"data pockets\" that are not necessarily causal. This result suggests important implications, allowing us to unify many existing approaches in a single method that can mix and match different assumptions, including non-causal ones, based on the invariances relevant to our application. It also significantly benefits applicability, which we demonstrate by improving treatment effect estimation on real-world high-dimensional ecological data. Overall, this paper clarifies the role of causality assumptions in the discovery of causal variables and shifts the focus to preserving data symmetries.","lang":"eng"}]},{"publication_identifier":{"isbn":["9798331320850"]},"publication_status":"published","publication":"13th International Conference on Learning Representations","date_created":"2025-07-20T22:02:01Z","file":[{"access_level":"open_access","checksum":"64cfdb12ae3e4e8ba57b1403e1066776","file_size":732745,"file_id":"20065","file_name":"2025_ICLR_Chen.pdf","date_updated":"2025-07-22T07:58:22Z","success":1,"date_created":"2025-07-22T07:58:22Z","creator":"dernst","content_type":"application/pdf","relation":"main_file"}],"date_updated":"2025-08-04T08:03:11Z","type":"conference","year":"2025","conference":{"location":"Singapore, Singapore","end_date":"2025-04-28","name":"ICLR: International Conference on Learning Representations","start_date":"2025-04-24"},"related_material":{"link":[{"relation":"software","url":"https://github.com/IST-DASLab/ScalableMNN"}]},"title":"Scalable mechanistic neural networks","article_processing_charge":"No","month":"04","oa":1,"oa_version":"Published Version","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["000"],"quality_controlled":"1","corr_author":"1","has_accepted_license":"1","page":"63716-63737","date_published":"2025-04-01T00:00:00Z","OA_place":"publisher","scopus_import":"1","_id":"20032","department":[{"_id":"DaAl"},{"_id":"FrLo"}],"citation":{"apa":"Chen, J., Yao, D., Pervez, A. A., Alistarh, D.-A., &#38; Locatello, F. (2025). Scalable mechanistic neural networks. In <i>13th International Conference on Learning Representations</i> (pp. 63716–63737). Singapore, Singapore: ICLR.","short":"J. Chen, D. Yao, A.A. Pervez, D.-A. Alistarh, F. Locatello, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 63716–63737.","ieee":"J. Chen, D. Yao, A. A. Pervez, D.-A. Alistarh, and F. Locatello, “Scalable mechanistic neural networks,” in <i>13th International Conference on Learning Representations</i>, Singapore, Singapore, 2025, pp. 63716–63737.","mla":"Chen, Jiale, et al. “Scalable Mechanistic Neural Networks.” <i>13th International Conference on Learning Representations</i>, ICLR, 2025, pp. 63716–37.","ama":"Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. Scalable mechanistic neural networks. In: <i>13th International Conference on Learning Representations</i>. ICLR; 2025:63716-63737.","chicago":"Chen, Jiale, Dingling Yao, Adeel A Pervez, Dan-Adrian Alistarh, and Francesco Locatello. “Scalable Mechanistic Neural Networks.” In <i>13th International Conference on Learning Representations</i>, 63716–37. ICLR, 2025.","ista":"Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. 2025. Scalable mechanistic neural networks. 13th International Conference on Learning Representations. ICLR: International Conference on Learning Representations, 63716–63737."},"publisher":"ICLR","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"arxiv":["2410.06074"]},"abstract":[{"text":"We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) (Pervez et al., 2024), we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. This significant improvement enables efficient modeling of long-term dynamics without sacrificing accuracy or interpretability. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources. Consequently, S-MNN can drop-in replace the original MNN in applications, providing a practical and efficient tool for integrating mechanistic bottlenecks into neural network models of complex dynamical systems. Source code is available at https://github.com/IST-DASLab/ScalableMNN.","lang":"eng"}],"OA_type":"diamond","arxiv":1,"language":[{"iso":"eng"}],"author":[{"orcid":"0000-0001-5337-5875","first_name":"Jiale","id":"4d0a9064-1ff6-11ee-9fa6-ec046c604785","full_name":"Chen, Jiale","last_name":"Chen"},{"full_name":"Yao, Dingling","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","last_name":"Yao","first_name":"Dingling"},{"full_name":"Pervez, Adeel A","id":"fca6d90c-d47f-11ee-bc87-93ff51604981","last_name":"Pervez","first_name":"Adeel A"},{"last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian"},{"orcid":"0000-0002-4850-0683","first_name":"Francesco","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello"}],"file_date_updated":"2025-07-22T07:58:22Z","day":"01","status":"public"},{"day":"01","language":[{"iso":"eng"}],"author":[{"last_name":"Yao","full_name":"Yao, Dingling","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","first_name":"Dingling"},{"first_name":"Filip","last_name":"Tronarp","full_name":"Tronarp, Filip"},{"first_name":"Nathanael","full_name":"Bosch, Nathanael","last_name":"Bosch"}],"status":"public","abstract":[{"lang":"eng","text":"Filtering-based probabilistic numerical solvers for ordinary differential equations (ODEs), also known as ODE filters, have been established as efficient methods for quantifying numerical uncertainty in the solution of ODEs. In practical applications, however, the underlying dynamical system often contains uncertain parameters, requiring the propagation of this model uncertainty to the ODE solution. In this paper, we demonstrate that ODE filters, despite their probabilistic nature, do not automatically solve this uncertainty propagation problem. To address this limitation, we present a novel approach that combines ODE filters with numerical quadrature to properly marginalize over uncertain parameters, while accounting for both parameter uncertainty and numerical solver uncertainty. Experiments across multiple dynamical systems demonstrate that the resulting uncertainty estimates closely match reference solutions. Notably, we show\r\nhow the numerical uncertainty from the ODE solver can help prevent overconfidence in the propagated uncertainty estimates, especially when using larger step sizes. Our results illustrate that probabilistic numerical methods can effectively quantify both numerical and parametric uncertainty in dynamical systems. "}],"external_id":{"arxiv":["2503.04684"]},"OA_type":"green","arxiv":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","alternative_title":["PMLR"],"citation":{"ista":"Yao D, Tronarp F, Bosch N. 2025. Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers. Proceedings of the 1st International Conference on Probabilistic Numerics. ProbNum: Conference on Probabilistic Numerics, PMLR, vol. 271.","chicago":"Yao, Dingling, Filip Tronarp, and Nathanael Bosch. “Propagating Model Uncertainty through Filtering-Based Probabilistic Numerical ODE Solvers.” In <i>Proceedings of the 1st International Conference on Probabilistic Numerics</i>, Vol. 271. ML Research Press, 2025.","mla":"Yao, Dingling, et al. “Propagating Model Uncertainty through Filtering-Based Probabilistic Numerical ODE Solvers.” <i>Proceedings of the 1st International Conference on Probabilistic Numerics</i>, vol. 271, ML Research Press, 2025.","ieee":"D. Yao, F. Tronarp, and N. Bosch, “Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers,” in <i>Proceedings of the 1st International Conference on Probabilistic Numerics</i>, Sophia Antipolis, France, 2025, vol. 271.","ama":"Yao D, Tronarp F, Bosch N. Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers. In: <i>Proceedings of the 1st International Conference on Probabilistic Numerics</i>. Vol 271. ML Research Press; 2025.","short":"D. Yao, F. Tronarp, N. Bosch, in:, Proceedings of the 1st International Conference on Probabilistic Numerics, ML Research Press, 2025.","apa":"Yao, D., Tronarp, F., &#38; Bosch, N. (2025). Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers. In <i>Proceedings of the 1st International Conference on Probabilistic Numerics</i> (Vol. 271). Sophia Antipolis, France: ML Research Press."},"acknowledgement":"NB gratefully acknowledge co-funding by the European Union (ERC, ANUBIS, 101123955. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them). NB thanks the International\r\nMax Planck Research School for Intelligent Systems (IMPRS-IS) for their support.","_id":"20592","scopus_import":"1","department":[{"_id":"FrLo"}],"publisher":"ML Research Press","has_accepted_license":"1","oa_version":"Preprint","tmp":{"image":"/images/cc_by_sa.png","legal_code_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","name":"Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)","short":"CC BY-SA (4.0)"},"ddc":["000"],"quality_controlled":"1","main_file_link":[{"url":"https://openreview.net/forum?id=sgPCP9jOlS","open_access":"1"}],"date_published":"2025-01-01T00:00:00Z","OA_place":"repository","article_processing_charge":"No","title":"Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers","license":"https://creativecommons.org/licenses/by-sa/4.0/","oa":1,"month":"01","volume":271,"conference":{"location":"Sophia Antipolis, France","end_date":"2025-09-03","name":"ProbNum: Conference on Probabilistic Numerics","start_date":"2025-09-01"},"intvolume":"       271","date_updated":"2025-11-10T08:33:11Z","publication_identifier":{"eissn":["2640-3498"]},"publication":"Proceedings of the 1st International Conference on Probabilistic Numerics","publication_status":"published","date_created":"2025-11-02T23:01:35Z","type":"conference","year":"2025"},{"title":"The third pillar of causal analysis? A measurement perspective on causal representations","article_processing_charge":"No","month":"12","oa":1,"oa_version":"Preprint","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2505.17708","open_access":"1"}],"ddc":["000"],"corr_author":"1","has_accepted_license":"1","date_published":"2025-12-15T00:00:00Z","OA_place":"repository","publication_identifier":{"issn":["1049-5258"]},"publication_status":"published","publication":"39th Annual Conference on Neural Information Processing Systems","date_created":"2026-01-29T14:24:56Z","date_updated":"2026-07-28T07:14:27Z","type":"conference","year":"2025","volume":38,"conference":{"location":"San Diego, CA, United States","end_date":"2025-12-07","name":"NeurIPS: Neural Information Processing Systems","start_date":"2025-12-02"},"related_material":{"link":[{"url":"https://github.com/shimenghuang/a-measurement-perspective-of-crl","relation":"software"}]},"intvolume":"        38","external_id":{"arxiv":["2505.17708"]},"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","arxiv":1,"language":[{"iso":"eng"}],"author":[{"last_name":"Yao","full_name":"Yao, Dingling","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","first_name":"Dingling"},{"last_name":"Huang","full_name":"Huang, Shimeng","id":"989c2a06-fb4e-11ef-a992-ab766442255b","first_name":"Shimeng","orcid":"0000-0001-6919-821X"},{"first_name":"Riccardo","id":"0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b","full_name":"Cadei, Riccardo","last_name":"Cadei"},{"last_name":"Zhang","full_name":"Zhang, Kun","first_name":"Kun"},{"last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","first_name":"Francesco"}],"day":"15","status":"public","department":[{"_id":"FrLo"}],"_id":"21068","citation":{"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.","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.","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.","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.","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.","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.","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."},"das_tickbox":"1","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","publisher":"Neural Information Processing Systems Foundation","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","alternative_title":["Advances in Neural Information Processing Systems"]},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Curran Associates","_id":"14946","department":[{"_id":"FrLo"}],"acknowledgement":"This work was initiated at the Second Bellairs Workshop on Causality held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop participants for providing a stimulating research environment. Further, we thank Cian Eastwood, Luigi Gresele, Stefano Soatto, Marco Bagatella and A. René Geist for helpful discussion. GM is a member of the Machine Learning Cluster of Excellence, EXC number 2064/1 – Project number 390727645. JvK and GM acknowledge support from the German Federal Ministry of Education and Research (BMBF) through the Tübingen AI Center (FKZ: 01IS18039B). The research of DX and SM was supported by the Air Force Office of Scientific Research under award number FA8655-22-1-7155. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force. We also thank SURF for the support in using the Dutch National Supercomputer Snellius. SL was supported by an IVADO excellence PhD scholarship and by Samsung Electronics Co., Ldt. DY was supported by an Amazon fellowship, the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the ISTA graduate school. Work done outside of Amazon.","citation":{"short":"D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, F. Locatello, in:, 12th International Conference on Learning Representations, Curran Associates, 2024.","apa":"Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G., … Locatello, F. (2024). Multi-view causal representation learning with partial observability. In <i>12th International Conference on Learning Representations</i>. Vienna, Austria: Curran Associates.","ieee":"D. Yao <i>et al.</i>, “Multi-view causal representation learning with partial observability,” in <i>12th International Conference on Learning Representations</i>, Vienna, Austria, 2024.","mla":"Yao, Dingling, et al. “Multi-View Causal Representation Learning with Partial Observability.” <i>12th International Conference on Learning Representations</i>, Curran Associates, 2024.","ama":"Yao D, Xu D, Lachapelle S, et al. Multi-view causal representation learning with partial observability. In: <i>12th International Conference on Learning Representations</i>. Curran Associates; 2024.","chicago":"Yao, Dingling, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, and Francesco Locatello. “Multi-View Causal Representation Learning with Partial Observability.” In <i>12th International Conference on Learning Representations</i>. Curran Associates, 2024.","ista":"Yao D, Xu D, Lachapelle S, Magliacane S, Taslakian P, Martius G, Kügelgen J von, Locatello F. 2024. Multi-view causal representation learning with partial observability. 12th International Conference on Learning Representations. ICLR: International Conference on Learning Representations."},"status":"public","author":[{"id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling","last_name":"Yao","first_name":"Dingling"},{"first_name":"Danru","full_name":"Xu, Danru","last_name":"Xu"},{"full_name":"Lachapelle, Sébastien","last_name":"Lachapelle","first_name":"Sébastien"},{"first_name":"Sara","last_name":"Magliacane","full_name":"Magliacane, Sara"},{"first_name":"Perouz","last_name":"Taslakian","full_name":"Taslakian, Perouz"},{"first_name":"Georg","full_name":"Martius, Georg","last_name":"Martius"},{"last_name":"Kügelgen","full_name":"Kügelgen, Julius von","first_name":"Julius von"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco"}],"language":[{"iso":"eng"}],"day":"07","file_date_updated":"2025-02-04T12:34:23Z","arxiv":1,"OA_type":"green","abstract":[{"text":"We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partially observed setting in which each view constitutes a nonlinear mixture of a subset of underlying latent variables, which can be causally related. We prove that the information shared across all subsets of any number of views can be learned up to a smooth bijection using contrastive learning and a single encoder per view. We also provide graphical criteria indicating which latent variables can be identified through a simple set of rules, which we refer to as identifiability algebra. Our general framework and theoretical results unify and extend several previous work on multi-view nonlinear ICA, disentanglement, and causal representation learning. We experimentally validate our claims on numerical, image, and multi-modal data sets. Further, we demonstrate that the performance of prior methods is recovered in different special cases of our setup. Overall, we find that access to multiple partial views offers unique opportunities for identifiable representation learning, enabling the discovery of latent structures from purely observational data.","lang":"eng"}],"external_id":{"arxiv":["2311.04056"]},"conference":{"name":"ICLR: International Conference on Learning Representations","start_date":"2024-05-07","location":"Vienna, Austria","end_date":"2024-05-07"},"year":"2024","type":"conference","file":[{"relation":"main_file","content_type":"application/pdf","creator":"dernst","date_updated":"2025-02-04T12:34:23Z","success":1,"file_name":"2024_ICLR_Yao.pdf","date_created":"2025-02-04T12:34:23Z","access_level":"open_access","file_size":1713606,"file_id":"18995","checksum":"8ed3c34706eeec622c7e8968dc0f747a"}],"date_created":"2024-02-07T14:28:34Z","publication_status":"published","publication":"12th International Conference on Learning Representations","date_updated":"2025-02-11T10:34:32Z","OA_place":"repository","date_published":"2024-11-07T00:00:00Z","ddc":["000"],"quality_controlled":"1","oa_version":"Published Version","has_accepted_license":"1","corr_author":"1","month":"11","oa":1,"title":"Multi-view causal representation learning with partial observability","article_processing_charge":"No"},{"related_material":{"link":[{"url":"https://github.com/CausalLearningAI/crl-dynamical-systems","relation":"software"}]},"conference":{"start_date":"2024-12-16","name":"NeurIPS: Neural Information Processing Systems","location":"Vancouver, Canada","end_date":"2024-12-16"},"volume":37,"intvolume":"        37","date_updated":"2025-07-10T11:51:32Z","file":[{"relation":"main_file","content_type":"application/pdf","creator":"dernst","date_created":"2025-02-05T07:44:58Z","file_name":"2024_NeurIPS_Yao.pdf","date_updated":"2025-02-05T07:44:58Z","success":1,"checksum":"fe8832367e7143876f178244385d859e","file_size":2595855,"file_id":"19006","access_level":"open_access"}],"date_created":"2025-02-05T07:49:00Z","publication":"38th Conference on Neural Information Processing Systems","publication_status":"published","year":"2024","type":"conference","has_accepted_license":"1","corr_author":"1","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["000","550"],"quality_controlled":"1","oa_version":"Published Version","OA_place":"publisher","date_published":"2024-12-01T00:00:00Z","article_processing_charge":"No","title":"Marrying causal representation learning with dynamical systems for science","oa":1,"month":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","alternative_title":["Advances in Neural Information Processing Systems"],"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. ","citation":{"short":"D. Yao, C.J. Muller, F. Locatello, in:, 38th Conference on Neural Information Processing Systems, 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.","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.","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.","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.","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."},"_id":"19005","department":[{"_id":"CaMu"},{"_id":"FrLo"}],"scopus_import":"1","publisher":"Neural Information Processing Systems Foundation","day":"01","file_date_updated":"2025-02-05T07:44:58Z","author":[{"id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling","last_name":"Yao","first_name":"Dingling"},{"orcid":"0000-0001-5836-5350","first_name":"Caroline J","last_name":"Muller","full_name":"Muller, Caroline J","id":"f978ccb0-3f7f-11eb-b193-b0e2bd13182b"},{"last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","first_name":"Francesco","orcid":"0000-0002-4850-0683"}],"language":[{"iso":"eng"}],"status":"public","OA_type":"gold","external_id":{"arxiv":["2405.13888"]},"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},{"type":"conference","year":"2023","date_updated":"2025-02-04T12:37:34Z","publication_status":"published","publication":"Causal Representation Learning Workshop at NeurIPS 2023","file":[{"date_created":"2024-02-13T08:50:53Z","file_name":"2023_CRL_Xu.pdf","date_updated":"2024-02-13T08:50:53Z","success":1,"checksum":"484efc27bda75ed6666044989695d9b6","file_id":"14982","file_size":552357,"access_level":"open_access","relation":"main_file","creator":"dernst","content_type":"application/pdf"}],"date_created":"2024-02-07T15:17:51Z","conference":{"end_date":"2023-12-15","location":"New Orleans, LA, United States","name":"CRL: Causal Representation Learning Workshop at NeurIPS","start_date":"2023-12-15"},"oa":1,"month":"12","article_processing_charge":"No","title":"A sparsity principle for partially observable causal representation learning","date_published":"2023-12-05T00:00:00Z","OA_place":"repository","has_accepted_license":"1","oa_version":"Published Version","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","ddc":["000"],"main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=Whr6uobelR"}],"publisher":"OpenReview","citation":{"mla":"Xu, Danru, et al. “A Sparsity Principle for Partially Observable Causal Representation Learning.” <i>Causal Representation Learning Workshop at NeurIPS 2023</i>, 54, OpenReview, 2023.","ieee":"D. Xu <i>et al.</i>, “A sparsity principle for partially observable causal representation learning,” in <i>Causal Representation Learning Workshop at NeurIPS 2023</i>, New Orleans, LA, United States, 2023.","ama":"Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable causal representation learning. In: <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. OpenReview; 2023.","short":"D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview, 2023.","apa":"Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello, F., &#38; Magliacane, S. (2023). A sparsity principle for partially observable causal representation learning. In <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. New Orleans, LA, United States: OpenReview.","ista":"Xu D, Yao D, Lachapelle S, Taslakian P, von Kügelgen J, Locatello F, Magliacane S. 2023. A sparsity principle for partially observable causal representation learning. Causal Representation Learning Workshop at NeurIPS 2023. CRL: Causal Representation Learning Workshop at NeurIPS, 54.","chicago":"Xu, Danru, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, and Sara Magliacane. “A Sparsity Principle for Partially Observable Causal Representation Learning.” In <i>Causal Representation Learning Workshop at NeurIPS 2023</i>. OpenReview, 2023."},"acknowledgement":"This work was initiated at the Second Bellairs Workshop on Causality held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop participants for providing a stimulating research environment. The research of DX and SM was supported by the Air Force Office of Scientific Research under award number FA8655-22-1-7155. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force. We also thank SURF for the support in using the Dutch National Supercomputer Snellius. DY was supported by an Amazon fellowship and the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Work done outside of Amazon. SL was supported by an IVADO excellence PhD scholarship and by Samsung Electronics Co., Ldt. JvK acknowledges support from the German Federal Ministry of Education and Research (BMBF)\r\nthrough the Tübingen AI Center (FKZ: 01IS18039B).\r\n","department":[{"_id":"FrLo"}],"_id":"14958","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"54","abstract":[{"lang":"eng","text":"Causal representation learning (CRL) aims at identifying high-level causal variables from low-level data, e.g. images. Current methods usually assume that all causal variables are captured in the high-dimensional observations. In this work, we focus on learning causal representations from data under partial observability, i.e., when some of the causal variables are not observed in the measurements, and the set of masked variables changes across the different samples. We introduce some initial theoretical results for identifying causal variables under partial observability by exploiting a sparsity regularizer, focusing in particular on the linear and piecewise linear mixing function case. We provide a theorem that allows us to identify the causal variables up to permutation and element-wise linear transformations in the linear case and a lemma that allows us to identify causal variables up to linear transformation in the piecewise case. Finally, we provide a conjecture that would allow us to identify the causal variables up to permutation and element-wise linear transformations also in the piecewise linear case. We test the theorem and conjecture on simulated data, showing the effectiveness of our method."}],"OA_type":"green","status":"public","file_date_updated":"2024-02-13T08:50:53Z","day":"05","language":[{"iso":"eng"}],"author":[{"full_name":"Xu, Danru","last_name":"Xu","first_name":"Danru"},{"first_name":"Dingling","last_name":"Yao","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","full_name":"Yao, Dingling"},{"full_name":"Lachapelle, Sebastien","last_name":"Lachapelle","first_name":"Sebastien"},{"first_name":"Perouz","last_name":"Taslakian","full_name":"Taslakian, Perouz"},{"first_name":"Julius","last_name":"von Kügelgen","full_name":"von Kügelgen, Julius"},{"orcid":"0000-0002-4850-0683","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello"},{"last_name":"Magliacane","full_name":"Magliacane, Sara","first_name":"Sara"}]}]