A Unified Framework for the Transportability of Population-Level Causal Measures
总体层面因果效应度量可迁移性的统一框架AI 解读 ↓
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总体层面因果效应度量可迁移性的统一框架
- 研究问题
- 当试验样本与目标总体存在协变量分布差异时，如何将风险差、风险比、优势比等多种总体因果效应度量迁移到目标总体？
- 方法与思路
- 作者在协变量漂移下构造涵盖一阶矩因果度量的统一框架，给出两类条件交换性假设下的识别结果，并推导经典及半参数估计量。
- 材料陈述的贡献
- 摘要称，框架覆盖多种效应度量，并揭示估计方法随度量而异：一步法与估计方程法对风险差较相近，对优势比则得到不同估计量。
- 阅读关注点
- 应核对两类条件交换性假设的具体差异、目标总体的定义，以及不同因果度量的识别公式、估计方程和理论与应用比较结果。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未列出具体识别条件与数值结果，不能据此判断任一估计方法普遍更优。
原文摘要
Generalization methods offer a powerful solution to one of the key drawbacks of randomized controlled trials (RCTs): their limited representativeness. By enabling the transport of treatment effect estimates to target populations subject to distributional shifts, these methods are increasingly recognized as the future of meta-analysis, the current gold standard in evidence-based medicine. Yet most existing approaches focus on the risk difference, overlooking the diverse range of causal measures routinely reported in clinical research. Reporting multiple effect measures-both absolute (e.g., risk difference, number needed to treat) and relative (e.g., risk ratio, odds ratio)-is essential to ensure clinical relevance, policy utility, and interpretability across contexts. To address this gap, we propose a unified framework for transporting a broad class of first-moment population causal effect measures under covariate shift. We provide identification results under two conditional exchangeability assumptions, derive both classical and semiparametric estimators, and evaluate their performance through theoretical analysis, simulations, and real-world applications. Our analysis shows the specificity of different causal measures and thus the interest of studying them all: for instance, two common approaches (one-step, estimating equation) lead to similar estimators for the risk difference but to two distinct estimators for the odds ratio.