病例对照抽样下的回顾性因果归因

中文摘要 AI 辅助

必要性概率PN量化了暴露个体在经历结局的情况下，若未暴露则不会经历该结局的概率。病例对照研究是调查病因学问题的重要资源，但其抽样设计可能引入选择偏倚，并使该概率的统计推断复杂化。在本文中，我们开发了一个非参数框架，用于在病例对照抽样下识别和有效估计PN。借助外部提供的总体结局患病率，我们推导出一个精确的识别公式，该公式在标准因果假设和单调性下识别PN，并在无单调性时给出有效下界。对于罕见结局，我们推导出一个更易处理的近似式，该近似式无需外部患病率信息，并证明其近似误差以总体结局患病率的阶数消失。我们进一步建立了精确和近似泛函的半参数效率理论，提出了渐近有效的估计量，并为相应目标构建置信区间。所提出的方法在利用回顾性数据调查因果归因的生物医学和流行病学研究中具有潜在应用价值。

英文摘要

The probability of necessity PN quantifies the probability that an exposed individual who experienced an outcome would not have experienced it in the absence of exposure. Case-control studies are an important resource for investigating etiologic questions, but their sampling design can introduce selection bias and complicate the statistical inference for PN.In this paper, we develop a nonparametric framework for identification and efficient estimation of PN under case-control sampling. With an externally supplied population outcome prevalence, we derive an exact identification formula that identifies PN under standard causal assumptions and monotonicity and yields a valid lower bound without monotonicity. For rare outcomes, we derive a more tractable approximation that requires no external prevalence information and prove that its approximation error vanishes at the order of the population outcome prevalence. We further establish the semiparametric efficiency theory for the exact and approximate functionals, propose asymptotically efficient estimators, and construct confidence intervals for the corresponding targets. The proposed approach has potential applications in biomedical and epidemiological studies where causal attribution is investigated using retrospective data.

发表机构

• University of Science and Technology of China（中国科学技术大学）
• City University of Hong Kong（香港城市大学）

机构由 AI 辅助整理，请以论文原文为准。