Prediction-Powered Data Fusion for Treatment Effect Estimation
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 ...
| Authors | Yonghan Jung, Shu Yang |
|---|---|
| Primary category | stat.ML |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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