---
OA_place: repository
OA_type: green
_id: '22832'
abstract:
- lang: eng
text: "In many scientific experiments, the data annotating cost constraints the
pace\r\nfor testing novel hypotheses. Yet, modern machine learning pipelines offer
a\r\npromising solution—provided their predictions yield correct conclusions.
We\r\nfocus on Prediction-Powered Causal Inferences (PPCI), i.e., estimating the\r\ntreatment
effect in an unlabeled target experiment, relying on training data with\r\nthe
same outcome annotated but potentially different treatment or effect modifiers.\r\nWe
first show that conditional calibration guarantees valid PPCI at population\r\nlevel.
Then, we introduce a sufficient representation constraint transferring validity\r\nacross
experiments, which we propose to enforce in practice in Deconfounded\r\nEmpirical
Risk Minimization, our new model-agnostic training objective. We\r\nvalidate our
method on synthetic and real-world scientific data, solving impossible\r\nproblem
instances for Empirical Risk Minimization even with standard invariance\r\nconstraints.
In particular, for the first time, we achieve valid causal inference\r\non a scientific
experiment with complex recording and no human annotations,\r\nfine-tuning a foundational
model on our similar annotated experiment."
acknowledgement: "We thank the Causal Learning and Artificial Intelligence group at
ISTA for the continuous feedback\r\non the project and valuable discussions. We
thank the Social Immunity group at ISTA, particularly\r\nJinook Oh, for the annotation
program and Michaela Hoenigsberger for supporting our ecological\r\nexperiment.
Riccardo Cadei is supported by a Google Research Scholar Award and a Google Initiated\r\nGift
to Francesco Locatello. This research was funded in part by the Austrian Science
Fund (FWF)\r\n10.55776/COE12). It was further partially supported by the ISTA Interdisciplinary
Project Committee\r\nfor the collaborative project “ALED” between Francesco Locatello
and Sylvia Cremer. For open\r\naccess purposes, the author has applied a CC BY public
copyright license to any author accepted\r\nmanuscript version arising from this
submission.\r\n"
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Riccardo
full_name: Cadei, Riccardo
id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
last_name: Cadei
- first_name: Ilker
full_name: Demirel, Ilker
last_name: Demirel
- first_name: Piersilvio
full_name: De Bartolomeis, Piersilvio
last_name: De Bartolomeis
- first_name: Lukas
full_name: Lindorfer, Lukas
id: 85f0e6d3-06b3-11ec-8982-8c5049fa4455
last_name: Lindorfer
- first_name: Sylvia
full_name: Cremer, Sylvia
id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87
last_name: Cremer
orcid: 0000-0002-2193-3868
- first_name: Cordelia
full_name: Schmid, Cordelia
last_name: Schmid
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Cadei R, Demirel I, De Bartolomeis P, et al. Prediction-Powered Causal Inferences.
In: 39th Conference on Neural Information Processing Systems. Vol 38. Neural
Information Processing Systems Foundation; 2025:82200-82229. doi:10.52202/085713-2479'
apa: 'Cadei, R., Demirel, I., De Bartolomeis, P., Lindorfer, L., Cremer, S., Schmid,
C., & Locatello, F. (2025). Prediction-Powered Causal Inferences. In 39th
Conference on Neural Information Processing Systems (Vol. 38, pp. 82200–82229).
San Diego, CA, United States: Neural Information Processing Systems Foundation.
https://doi.org/10.52202/085713-2479'
chicago: Cadei, Riccardo, Ilker Demirel, Piersilvio De Bartolomeis, Lukas Lindorfer,
Sylvia Cremer, Cordelia Schmid, and Francesco Locatello. “Prediction-Powered Causal
Inferences.” In 39th Conference on Neural Information Processing Systems,
38:82200–229. Neural Information Processing Systems Foundation, 2025. https://doi.org/10.52202/085713-2479.
ieee: R. Cadei et al., “Prediction-Powered Causal Inferences,” in 39th
Conference on Neural Information Processing Systems, San Diego, CA, United
States, 2025, vol. 38, pp. 82200–82229.
ista: '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.'
mla: Cadei, Riccardo, et al. “Prediction-Powered Causal Inferences.” 39th Conference
on Neural Information Processing Systems, vol. 38, Neural Information Processing
Systems Foundation, 2025, pp. 82200–29, doi:10.52202/085713-2479.
short: 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.
conference:
end_date: 2025-12-07
location: San Diego, CA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2025-12-02
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: "We share all the code implementation in the supplementary
material.\r\nOur new experimental ecology dataset preview is anonymously shared
on Figshare at\r\nhttps://figshare.com/s/9a490b6f6eeebd73350b. We further rely on
ISTAnt dataset publicly\r\navailable at https://doi.org/10.6084/m9.figshare.26484934.v2.
The synthetic experiments\r\non CausalMNIST relies on MNIST dataset LeCun [1998],
publicly available. Additional\r\nexperimental details are reported in Section 5
and Appendices B-C."
date_created: 2026-09-06T22:02:01Z
date_published: 2025-12-01T00:00:00Z
date_updated: 2026-09-17T06:56:40Z
day: '01'
department:
- _id: FrLo
- _id: GradSch
- _id: SyCr
doi: 10.52202/085713-2479
external_id:
arxiv:
- '2502.06343'
fulldoi: https://doi.org/10.52202/085713-2479
intvolume: ' 38'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2502.06343
month: '12'
oa: 1
oa_version: Preprint
page: 82200-82229
project:
- _id: a392d8f0-b034-11f1-b88e-d3025c6734f6
grant_number: COE12
name: Bilateral Artificial Intelligence (Locatello)
publication: 39th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9798331338275'
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Prediction-Powered Causal Inferences
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2025'
...