{"publication":"2nd Conference on Causal Learning and Reasoning","day":"12","publication_status":"published","date_created":"2023-08-22T14:20:18Z","date_published":"2023-04-12T00:00:00Z","type":"conference","conference":{"end_date":"2023-04-14","name":"CLeaR: Conference on Causal Learning and Reasoning","start_date":"2023-04-11","location":"Tübingen, Germany"},"citation":{"chicago":"Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","apa":"Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &#38; Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric causal representation learning. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","ieee":"Y. Liu <i>et al.</i>, “Causal triplet: An open challenge for intervention-centric causal representation learning,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","ama":"Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","short":"Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","ista":"Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023. Causal triplet: An open challenge for intervention-centric causal representation learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","mla":"Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023."},"_id":"14214","title":"Causal triplet: An open challenge for intervention-centric causal representation learning","month":"04","author":[{"first_name":"Yuejiang","full_name":"Liu, Yuejiang","last_name":"Liu"},{"last_name":"Alahi","full_name":"Alahi, Alexandre","first_name":"Alexandre"},{"first_name":"Chris","full_name":"Russell, Chris","last_name":"Russell"},{"full_name":"Horn, Max","last_name":"Horn","first_name":"Max"},{"full_name":"Zietlow, Dominik","last_name":"Zietlow","first_name":"Dominik"},{"last_name":"Schölkopf","full_name":"Schölkopf, Bernhard","first_name":"Bernhard"},{"first_name":"Francesco","full_name":"Locatello, Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"}],"date_updated":"2023-09-13T09:23:08Z","external_id":{"arxiv":["2301.05169"]},"article_processing_charge":"No","quality_controlled":"1","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2301.05169"}],"year":"2023","abstract":[{"text":"Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In this paper, we present Causal Triplet, a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual setting, where only certain object-level variables allow for counterfactual observations whereas others do not; (ii) an interventional downstream task with an emphasis on out-of-distribution robustness from the independent causal mechanisms principle. Through extensive experiments, we find that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts. However, recent causal representation learning methods still struggle to identify such latent structures, indicating substantial challenges and opportunities for future work.","lang":"eng"}],"oa":1,"department":[{"_id":"FrLo"}],"oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","extern":"1"}