arXiv cs.AI· Yonghan Jung, Shu Yang·· 9 天前AI 评分18
无需 OBS 特殊假设的预测驱动数据融合：AIPW-Fusion、DR-Fusion 与 R-Fusion 用于处理效应估计
Prediction-Powered Data Fusion for Treatment Effect Estimation
AI 导读
研究者提出一种数据融合框架，在不对观察性研究（OBS）做特殊假设的前提下，将小型随机对照试验（RCT）与大型 OBS 结合，在保留 RCT 估计无偏性的同时借助 OBS 提升精度。基于该框架构建了具有闭式权重与置信区间的 ATE 估计器 AIPW-Fusion，以及两个 CATE 学习器 DR-Fusion 和 R-Fusion。实验验证了上述方法的有效性，代码已公开。
正文 · 原文
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 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.
| Comments: | 35 pages. Code: this https URL |
| Subjects: | Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME) |
| Cite as: | arXiv:2610.12332 [stat.ML] |
| (or arXiv:2610.12332v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12332 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yonghan Jung [view email]
[v1]
Thu, 8 Oct 2026 17:11:49 UTC (501 KB)
来源：arXiv cs.AI · arxiv.org