Among recent developments in definitions and analysis of selection bias is the potential outcomes approach of Kenah (Epidemiology, 2023), which allows non-parametric analysis using single-world intervention graphs, linking selection of study participants to identification of causal effects. Mohan & Pearl (JASA, 2021) provide a framework for missing data via directed acyclic graphs augmented with nodes indicating missingness for each sometimes-missing variable, which allows for analysis of more general missing data problems but cannot easily encode scenarios in which different groups of variables are observed in specific subsamples. We give an alternative formulation of the potential outcomes framework based on conditional separable effects and indicators for selection into subsamples. This is practical for problems between the single-sample scenarios considered by Kenah and the variable-wise missingness considered by Mohan & Pearl. This simplifies identification conditions and admits generalizations to scenarios with multiple, potentially nested or overlapping study samples, as well as multiple or time-dependent exposures. We give examples of identifiability arguments for case-cohort studies, multiple or time-dependent exposures, and direct effects of selection.

翻译：在近期关于选择偏倚定义与分析的研究进展中，Kenah（《流行病学》，2023）提出的潜在结果框架允许使用单世界干预图进行非参数分析，将研究参与者的选择与因果效应的识别联系起来。Mohan与Pearl（《美国统计学会会刊》，2021）通过有向无环图构建了处理缺失数据的框架，该图通过附加节点表示每个可能缺失变量的缺失状态，能够分析更一般的缺失数据问题，但难以编码不同变量组在特定子样本中被观测到的场景。我们提出了一种基于条件可分离效应和子样本选择指标的潜在结果框架替代方案。该方案适用于介于Kenah考虑的单样本场景与Mohan和Pearl考虑的变量层面缺失之间的实际问题。这简化了识别条件，并可推广至具有多重（可能嵌套或重叠）研究样本、多重或时间依赖性暴露的场景。我们通过实例展示了病例队列研究、多重或时间依赖性暴露以及选择直接效应的可识别性论证。