Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment – such as a vaccine – given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial.

中文翻译：

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识别传染下的因果干预效应

定义和确定对传染性传染病结果的因果干预效果具有挑战性，因为给予一个人的治疗（例如疫苗）可能会影响其他人的感染结果。流行病学家提出了因果估计，以使用两人伙伴关系模型量化传染病下干预措施的效果。这些简单的概念模型帮助研究人员开发了与疫苗效果临床评估相关的因果估计。然而，许多这些伙伴关系模型是在排除现实传染病传播动态的结构性假设下制定的，限制了它们在经验干预试验中定义和识别因果治疗效果的概念实用性。在本文中，我们在任意传染病传播动态下提出了两人伙伴关系中的因果干预效应，并给出了非参数识别结果，显示了如何使用感染时间或二元结果数据在实证试验中估计影响。关键的见解是，传染是一种因果现象，它诱导感染结果的条件独立性，可用于识别具有临床意义的因果估计。将这些新估计值与现有数量进行比较，并使用 HIV 疫苗试验的真实模拟来说明结果。关键的见解是，传染是一种因果现象，它诱导感染结果的条件独立性，可用于识别具有临床意义的因果估计。将这些新估计值与现有数量进行比较，并使用 HIV 疫苗试验的真实模拟来说明结果。关键的见解是，传染是一种因果现象，它诱导感染结果的条件独立性，可用于识别具有临床意义的因果估计。将这些新估计值与现有数量进行比较，并使用 HIV 疫苗试验的真实模拟来说明结果。