面向处理效应估计的预测驱动数据融合方法
Prediction-Powered Data Fusion for Treatment Effect Estimation
AI 导读
一项 arXiv 研究提出预测驱动数据融合框架，在不对方差观测数据（OBS）施加特殊假设的前提下，将小规模随机对照试验（RCT）与大规模 OBS 融合，在保留 RCT 无偏性的同时借力大样本提升精度。基于该框架作者构建了闭式权重和置信区间的 ATE 估计器 AIPW-Fusion，以及两个 CATE 学习器 DR-Fusion 和 R-Fusion，实验验证了方法有效性。
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: | 39 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:2610.12332v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12332 arXiv-issued DOI via DataCite |
Submission history
From: Yonghan Jung [view email]
[v1]
Thu, 8 Oct 2026 17:11:49 UTC (501 KB)
[v2]
Fri, 23 Oct 2026 01:21:25 UTC (565 KB)
来源：arXiv cs.AI · arxiv.org