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arXiv AI By Yonghan Jung, Shu Yang

Prediction-Powered Data Fusion for Treatment Effect Estimation

Read the original on arXiv AI →

The paper introduces a framework that fuses data from a small randomized controlled trial (RCT) with a large observational study (OBS) to estimate treatment effects without imposing special assumptions on the OBS. It presents an ATE estimator called AIPW‑Fusion, which uses closed‑form weights and confidence intervals, and two conditional ATE (CATE) learners, DR‑Fusion and R‑Fusion, that preserve unbiasedness while borrowing precision from the OBS. Experiments validate the proposed methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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