发表机构
University of Illinois Urbana-Champaign; North Carolina State University（伊利诺伊大学厄巴纳-香槟分校; 北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有ATE、CATE估计器的缺陷，提出无需对OBS做特殊假设的预测驱动数据融合框架，构建了AIPW-Fusion、DR-Fusion、R-Fusion等估计器，实验验证了其有效性。
AI 中文摘要
随机对照试验（RCT）可识别无混杂的治疗效果，但样本量通常较小；而观察性研究（OBS）样本量大，但可能存在混杂。已有许多结合小型RCT与大型OBS的估计器被开发用于平均治疗效果（ATE）和条件平均治疗效果（CATE）。然而，现有的ATE估计器要么对OBS做出假设，要么未从OBS中借用足够的力量；CATE的研究少于ATE，现有CATE方法要么假设OBS无混杂，依赖混杂函数模型，要么接受偏差以换取更低方差。因此，我们提出一种框架，无需对OBS做特殊假设，通过保留基于RCT估计的无偏性，同时从大型OBS中借用力量以提升精度，来融合OBS与RCT。应用该原理，我们构建了具有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。实验验证了我们的发现。
英文摘要
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.
Comments35 pages. Code: https://github.com/CausalDataScience/DataFusionPPI
