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Prediction-Powered Data Fusion for Treatment Effect Estimation — arXiv:2610.12332

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.12332
License (as archived): CC0-1.0
Archived: 2026-10-09T06:03:54.115Z
SHA-256 of the archived text: d880108e4213d7a525f15a1625959857dd81e000ba170437c1a0de585aa80cdb
Stable URL of this page: https://metraipolis.com/documento/dc_abf69ecaedbd

How to cite

Prediction-Powered Data Fusion for Treatment Effect Estimation — arXiv:2610.12332 — arXiv (en), https://arxiv.org/abs/2610.12332 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_abf69ecaedbd (Metraipolis archive dc_abf69ecaedbd, sha256 d880108e4213d7a525f15a1625959857dd81e000ba170437c1a0de585aa80cdb)

BibTeX

@misc{dcabf69ecaedbd,
  title = {Prediction-Powered Data Fusion for Treatment Effect Estimation — arXiv:2610.12332},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.12332},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_abf69ecaedbd; sha256 d880108e4213d7a525f15a1625959857dd81e000ba170437c1a0de585aa80cdb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.12332. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Randomized controlled trials (RCTs) identify treatment effects without confounding but are often small, whereas observational studies (OBS) are large but may be confounded. Many estimators combining a small RCT with a large OBS have been developed for the average treatment effect (ATE) and the conditional ATE (CATE). However, existing ATE estimators either make assumptions on the OBS or do not borrow enough power from them. The CATE has been studied less than the ATE. Existing CATE methods either assume the OBS are unconfounded, rely on a model of the confounding function, or accept bias in exchange for lower variance. We therefore propose a framework that, without special assumptions on the OBS, fuses the OBS and the RCT by preserving the unbiasedness of RCT-based estimation while borrowing power from the large OBS to boost precision. Applying this principle, we build an ATE estimator, AIPW-Fusion, with closed-form weights and confidence intervals, and two CATE learners, DR-Fusion and R-Fusion. Experiments corroborate our findings.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (stat.ML) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2610.12332 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T05:47:02.192Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).