[{"year":"2025","abstract":[{"text":"In many scientific experiments, the data annotating cost constraints the pace for testing novel hypotheses. Yet, modern machine learning pipelines offer a promising solution—provided their predictions yield correct conclusions. We focus on Prediction-Powered Causal Inferences (PPCI), i.e., estimating the treatment effect in an unlabeled target experiment, relying on training data with the same outcome annotated but potentially different treatment or effect modifiers. We first show that conditional calibration guarantees valid PPCI at population level. Then, we introduce a sufficient representation constraint transferring validity across experiments, which we propose to enforce in practice in Deconfounded Empirical Risk Minimization, our new model-agnostic training objective. We validate our method on synthetic and real-world scientific data, solving impossible problem instances for Empirical Risk Minimization even with standard invariance constraints. In particular, for the first time, we achieve valid causal inference on a scientific experiment with complex recording and no human annotations, fine-tuning a foundational model on our similar annotated experiment.","lang":"eng"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"acknowledgement":"We thank the Causal Learning and Artificial Intelligence group at ISTA for the continuous feedback on the project and valuable discussions. We thank the Social Immunity group at ISTA, particularly Jinook Oh, for the annotation program and Michaela Hoenigsberger for supporting our ecological experiment. Riccardo Cadei is supported by a Google Research Scholar Award and a Google Initiated Gift to Francesco Locatello. This research was funded in part by the Austrian Science Fund (FWF) 10.55776/COE12). It was further partially supported by the ISTA Interdisciplinary Project Committee for the collaborative project “ALED” between Francesco Locatello and Sylvia Cremer. For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission.","_id":"21076","OA_type":"gold","article_processing_charge":"No","day":"15","date_published":"2025-12-15T00:00:00Z","oa":1,"status":"public","file":[{"content_type":"application/pdf","checksum":"92467fa566cd36671a6a3b9e71ae0f71","access_level":"open_access","relation":"main_file","date_created":"2026-01-29T14:35:02Z","creator":"flocatel","success":1,"file_id":"21077","date_updated":"2026-01-29T14:35:02Z","file_size":8489023,"file_name":"17546_Prediction_Powered_Causa.pdf"}],"publication_status":"published","citation":{"short":"R. Cadei, I. Demirel, P. De Bartolomeis, L. Lindorfer, S. Cremer, C. Schmid, F. Locatello, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.","ama":"Cadei R, Demirel I, De Bartolomeis P, et al. Prediction-powered causal inferences. In: <i>39th Annual Conference on Neural Information Processing Systems</i>. Vol 38. Neural Information Processing Systems Foundation; 2025.","apa":"Cadei, R., Demirel, I., De Bartolomeis, P., Lindorfer, L., Cremer, S., Schmid, C., &#38; Locatello, F. (2025). Prediction-powered causal inferences. In <i>39th Annual Conference on Neural Information Processing Systems</i> (Vol. 38). San Diego, CA, United States: Neural Information Processing Systems Foundation.","mla":"Cadei, Riccardo, et al. “Prediction-Powered Causal Inferences.” <i>39th Annual Conference on Neural Information Processing Systems</i>, vol. 38, Neural Information Processing Systems Foundation, 2025.","ista":"Cadei R, Demirel I, De Bartolomeis P, Lindorfer L, Cremer S, Schmid C, Locatello F. 2025. Prediction-powered causal inferences. 39th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 38.","ieee":"R. Cadei <i>et al.</i>, “Prediction-powered causal inferences,” in <i>39th Annual Conference on Neural Information Processing Systems</i>, San Diego, CA, United States, 2025, vol. 38.","chicago":"Cadei, Riccardo, Ilker Demirel, Piersilvio De Bartolomeis, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, and Francesco Locatello. “Prediction-Powered Causal Inferences.” In <i>39th Annual Conference on Neural Information Processing Systems</i>, Vol. 38. Neural Information Processing Systems Foundation, 2025."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2026-01-29T14:35:11Z","publisher":"Neural Information Processing Systems Foundation","author":[{"last_name":"Cadei","full_name":"Cadei, Riccardo","first_name":"Riccardo","id":"0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b"},{"first_name":"Ilker","full_name":"Demirel, Ilker","last_name":"Demirel"},{"first_name":"Piersilvio","full_name":"De Bartolomeis, Piersilvio","last_name":"De Bartolomeis"},{"last_name":"Lindorfer","full_name":"Lindorfer, Lukas","id":"85f0e6d3-06b3-11ec-8982-8c5049fa4455","first_name":"Lukas"},{"first_name":"Sylvia","id":"2F64EC8C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2193-3868","last_name":"Cremer","full_name":"Cremer, Sylvia"},{"last_name":"Schmid","full_name":"Schmid, Cordelia","first_name":"Cordelia"},{"last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"}],"das_tickbox":"1","intvolume":"        38","OA_place":"publisher","alternative_title":["Advances in Neural Information Processing Systems"],"date_updated":"2026-07-28T07:20:10Z","department":[{"_id":"FrLo"},{"_id":"SyCr"}],"quality_controlled":"1","type":"conference","ddc":["000"],"title":"Prediction-powered causal inferences","conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2025-12-07","start_date":"2025-12-02","location":"San Diego, CA, United States"},"language":[{"iso":"eng"}],"file_date_updated":"2026-01-29T14:35:02Z","month":"12","publication":"39th Annual Conference on Neural Information Processing Systems","has_accepted_license":"1","volume":38,"publication_identifier":{"issn":["1049-5258"]},"oa_version":"Published Version"}]