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. Particularly challenging is differential recall bias in the context of self-reported binary exposures, where the bias may be directional rather than random , and its extent varies according to the outcomes experienced. This paper makes several contributions: (1) it establishes bounds for the average treatment effect (ATE) even when a validation study is not available; (2) it proposes multiple estimation methods across various strategies predicated on different assumptions; and (3) it suggests a sensitivity analysis technique to assess the robustness of the causal conclusion, incorporating insights from prior research. The effectiveness of these methods is demonstrated through simulation studies that explore various model misspecification scenarios. These approaches are then applied to investigate the effect of childhood physical abuse on mental health in adulthood.

翻译：观察性研究常用于估计暴露或处理对结果的影响。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类型是回忆偏倚，这在回顾性队列研究中尤为突出，研究对象可能无法准确回忆过去的暴露情况。特别具有挑战性的是在自我报告二元暴露背景下的差异性回忆偏倚，这种偏倚可能是方向性的而非随机的，且其程度会因所经历的结果而异。本文作出以下贡献：(1) 即使在没有验证研究的情况下，也为平均处理效应(ATE)建立了边界；(2) 提出了基于不同假设的多种策略下的多种估计方法；(3) 提出了一种敏感性分析技术，结合先前研究的见解，以评估因果结论的稳健性。这些方法的有效性通过模拟研究得到验证，这些研究探索了各种模型设定错误的情景。随后，这些方法被应用于研究童年期身体虐待对成年期心理健康的影响。