发表机构
Tsinghua University; University of Florida; Harvard T.H. Chan School of Public Health; Zhejiang University; Harvard University; Peking University（清华大学; 佛罗里达大学; 哈佛陈曾熙公共卫生学院; 浙江大学; 哈佛大学; 北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对代表性不足亚群IV分析效率低和弱IV偏倚问题，提出KNIVES框架，通过知识迁移和偏倚校正提升效率与稳健性，并经数值实验和UK Biobank数据验证。
AI 中文摘要
工具变量（IV）分析在代表性不足的目标人群中，由于样本量有限，面临统计效率低和弱工具变量偏倚的问题。利用来自样本量更大的源人群的外部知识为解决该问题提供了有吸引力的方案。本文提出了面向代表性不足亚群的知识驱动工具变量估计器（KNIVES），该框架通过从外部源人群数据迁移知识来增强对代表性不足亚群的IV分析。KNIVES通过结合多个候选模型，并采用针对下游IV估计器量身定制的有效信噪比准则（而非仅基于预测精度）构建知识迁移的IV得分。为解决目标人群中IV得分与暴露之间的弱关联以及候选模型间的高相关性，KNIVES采用偏倚校正技术以减轻由此产生的偏倚并确保稳健性能。理论研究表明，我们的方法对弱IV偏倚具有稳健性，并且与基于任何单一候选模型的因果效应估计器相比，实现了更高的效率。此外，KNIVES通过保证其渐近方差不大于仅依赖目标人群数据的估计器，避免了负迁移。大量数值研究证明了我们的方法在有限样本下优于现有方法的性能。在英国生物银行数据中针对少数族裔亚群的两项孟德尔随机化研究进一步说明了我们方法的优势。
英文摘要
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.
