arXiv Artificial Intelligence

Prediction-Powered Data Fusion for Treatment Effect Estimation

1 hour ago 09.10.2026 07:00 👁 1 views
Prediction-Powered Data Fusion for Treatment Effect Estimation

Quick summary

arXiv:2610.12332v1 Announce Type: cross Abstract: 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

Key takeaways

  • arXiv:2610.12332v1 Announce Type: cross Abstract: 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.

Why it matters

“Prediction-Powered Data Fusion for Treatment Effect Estimation” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗