Instrumental Variable Analysis in Underrepresented Subpopulations Powered by Knowledge Transfer
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借助知识迁移增强代表性不足子群体的工具变量分析
- 研究问题
- 当目标子群体样本有限、工具变量较弱时，如何利用较大来源群体的信息提高因果效应估计效率，并避免负迁移？
- 方法与思路
- 作者提出 KNIVES，按面向工具变量估计的有效信噪比组合候选模型，并针对弱关联及候选模型高度相关进行偏差校正。
- 材料陈述的贡献
- 摘要称，方法可缓解弱工具变量偏差、提高效率，且渐近方差不超过仅使用目标数据的估计量；另提供数值与孟德尔随机化研究。
- 阅读关注点
- 应核对跨群体知识可迁移的条件、工具变量有效性、偏差校正推导，以及方差保证所需假设和弱工具变量模拟设计。
- 阅读边界
- 所给材料为 arXiv 预印本，不能据此确认同行评审或期刊接收。摘要未说明迁移条件与理论保证的具体适用范围。
原文摘要
Instrumental variable (IV) analysis in underrepresented target populations suffers from low statistical efficiency and weak IV bias due to limited sample size. Leveraging external knowledge from a source population with larger samples offers an appealing solution to this problem. We propose in this paper the KNowledge-powered IV Estimator for underrepresented Subpopulations (KNIVES), a framework that enhances the IV analysis for underrepresented subpopulations by transferring knowledge from external source population data. KNIVES constructs a knowledge-transferred IV score by combining multiple candidate models through an effective signal-to-noise ratio criterion tailored to the downstream IV estimator, rather than prediction accuracy alone. To address weak associations between the IV score and the exposure, as well as a high correlation among candidate models in the target population, KNIVES employs a bias correction technique to mitigate the resulting bias and ensure robust performance. Theoretical investigation demonstrates that our method is robust to weak IV bias and achieves improved efficiency compared to the causal effect estimator based on any single candidate model. In addition, KNIVES avoids negative transfer by guaranteeing that its asymptotic variance is not larger than that of the estimator relying solely on target population data. Extensive numerical studies demonstrate the superior finite-sample performance of our method over existing methods. Two Mendelian randomization studies on ethnic minority subgroups in UK Biobank data further illustrate the advantages of our method.