Retrospective Causal Attribution under Case-Control Sampling

2026-09-07

AI Summary

本文针对案例对照抽样中的选择偏差问题，提出了一种非参数框架来识别和有效估计概率必要性PN，并提供了渐近有效的估计方法。

Abstract

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.

Problem

• case-control sampling
• probability of necessity
• selection bias

Innovation

• nonparametric framework
• case-control sampling
• probability of necessity (PN)
• semiparametric efficiency theory
• rare outcomes