Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassification is recall bias, which occurs in retrospective cohort studies when study subjects may inaccurately recall their past exposure. Specifically, differential recall bias can be problematic when examining the effect of a self-reported binary exposure since the magnitude of recall bias can differ between groups. In this paper, we provide the following contributions: 1) we derive bounds for the average treatment effect (ATE) in the presence of recall bias; 2) we develop several estimation approaches under different identification strategies; 3) we conduct simulation studies to evaluate their performance under several scenarios of model misspecification; 4) we propose a sensitivity analysis method that can examine the robustness of our results with respect to different assumptions; and 5) we apply the proposed framework to an observational study, estimating the effect of childhood physical abuse on adulthood mental health.

翻译：观察性研究常用于估计暴露或处理对结局的影响。为获得处理效应的无偏估计，准确测量暴露至关重要。一种常见的暴露错误分类是回忆偏倚，其发生于回顾性队列研究中，当研究对象可能不准确地回忆其既往暴露时。具体而言，在检验自我报告的二分类暴露效应时，差异回忆偏倚可能成为问题，因为回忆偏倚的程度在不同组群间可能存在差异。本文做出以下贡献：1）我们推导了存在回忆偏倚时平均处理效应（ATE）的边界；2）我们基于不同的识别策略开发了若干估计方法；3）我们开展模拟研究以评估这些方法在若干模型设定错误情形下的表现；4）我们提出了一种敏感性分析方法，可检验我们的结果在不同假设下的稳健性；5）我们将所提出的框架应用于一项观察性研究，估计童年期身体虐待对成年期心理健康的影响。