Causal inference in connected populations is complicated by contagion and other real-world processes inducing dependence among outcomes. We address a gap in the literature on causal inference under contagion: while there is a growing body of work on estimating causal effects under contagion, little is known about how contagion impacts causal effects and inference. We provide insight into how contagion impacts causal effects and inference based on closed-form expressions for causal effects under contagion. These closed-form expressions reveal that the effects of interventions, spillover, and contagion are intertwined even in the simplest possible settings, and that contagion can decrease or increase causal effects. We discuss statistical implications, including asymptotic bias of model-based estimators ignoring dependence among outcomes due to contagion, violations of neighborhood exposure assumptions underlying design-based estimators by unrestricted contagion, and possible remedies.

翻译：连通群体中的因果推断因传染及诱发结果间依赖性的其他真实世界过程而复杂化。我们针对传染情境下因果推断文献中的空白展开研究：尽管关于传染条件下因果效应估计的研究日益增多，但关于传染如何影响因果效应及推断的认知仍十分有限。本文基于传染条件下因果效应的闭式表达式，深入揭示了传染对因果效应及推断的影响机制。这些闭式表达式表明，即便在最简设置中，干预效应、溢出效应与传染效应仍相互交织，且传染可能降低或增强因果效应。我们讨论了相关统计学意义，包括因忽略传染所致结果间依赖性的模型估计量的渐近偏差、无限制传染对基于设计估计量所需邻域暴露假设的违反，以及可能的修正方案。