conference paper published yes Riccardo Cadei author 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b Ilker Demirel author Piersilvio De Bartolomeis author Lukas Lindorfer author 85f0e6d3-06b3-11ec-8982-8c5049fa4455 Sylvia Cremer author 2F64EC8C-F248-11E8-B48F-1D18A9856A870000-0002-2193-3868 Cordelia Schmid author Francesco Locatello author 26cfd52f-2483-11ee-8040-88983bcc06d40000-0002-4850-0683 FrLo department GradSch department SyCr department NeurIPS: Neural Information Processing Systems Bilateral Artificial Intelligence (Locatello) project 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. Neural Information Processing Systems Foundation2025San Diego, CA, United States eng 1049-5258 9798331338275 2502.0634310.52202/085713-2479 3882200-82229 Cadei, R., Demirel, I., De Bartolomeis, P., Lindorfer, L., Cremer, S., Schmid, C., &#38; Locatello, F. (2025). Prediction-Powered Causal Inferences. In <i>39th Conference on Neural Information Processing Systems</i> (Vol. 38, pp. 82200–82229). San Diego, CA, United States: Neural Information Processing Systems Foundation. <a href="https://doi.org/10.52202/085713-2479">https://doi.org/10.52202/085713-2479</a> Cadei, Riccardo, Ilker Demirel, Piersilvio De Bartolomeis, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, and Francesco Locatello. “Prediction-Powered Causal Inferences.” In <i>39th Conference on Neural Information Processing Systems</i>, 38:82200–229. Neural Information Processing Systems Foundation, 2025. <a href="https://doi.org/10.52202/085713-2479">https://doi.org/10.52202/085713-2479</a>. R. Cadei, I. Demirel, P. De Bartolomeis, L. Lindorfer, S. Cremer, C. Schmid, F. Locatello, in:, 39th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025, pp. 82200–82229. Cadei, Riccardo, et al. “Prediction-Powered Causal Inferences.” <i>39th Conference on Neural Information Processing Systems</i>, vol. 38, Neural Information Processing Systems Foundation, 2025, pp. 82200–29, doi:<a href="https://doi.org/10.52202/085713-2479">10.52202/085713-2479</a>. Cadei R, Demirel I, De Bartolomeis P, Lindorfer L, Cremer S, Schmid C, Locatello F. 2025. Prediction-Powered Causal Inferences. 39th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 38, 82200–82229. R. Cadei <i>et al.</i>, “Prediction-Powered Causal Inferences,” in <i>39th Conference on Neural Information Processing Systems</i>, San Diego, CA, United States, 2025, vol. 38, pp. 82200–82229. Cadei R, Demirel I, De Bartolomeis P, et al. Prediction-Powered Causal Inferences. In: <i>39th Conference on Neural Information Processing Systems</i>. Vol 38. Neural Information Processing Systems Foundation; 2025:82200-82229. doi:<a href="https://doi.org/10.52202/085713-2479">10.52202/085713-2479</a> 228322026-09-06T22:02:01Z2026-09-17T06:56:40Z