VanderWeele Tyler J, Tchetgen Tchetgen Eric J, Halloran M Elizabeth
Departments of Epidemiology and Biostatistics, Harvard School of Public Health, University of Washington.
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center and Department of Biostatistics, University of Washington.
Stat Sci. 2014 Nov;29(4):687-706. doi: 10.1214/14-STS479.
Causal inference with interference is a rapidly growing area. The literature has begun to relax the "no-interference" assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which causal effects in the presence of interference are not identified, either because randomization alone does not suffice for identification, or because treatment is not randomized and there may be unmeasured confounders of the treatment-outcome relationship. We develop sensitivity analysis techniques for these settings. We describe several sensitivity analysis techniques for the infectiousness effect which, in a vaccine trial, captures the effect of the vaccine of one person on protecting a second person from infection even if the first is infected. We also develop two sensitivity analysis techniques for causal effects in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.
存在干扰情况下的因果推断是一个快速发展的领域。文献已开始放宽“无干扰”假设，即一个个体接受的治疗不会影响其他个体的结果。在本文中，我们简要回顾了在治疗已随机化的情况下存在干扰时的因果推断文献。然后，我们考虑这样的情形，即存在干扰时的因果效应无法识别，这要么是因为仅随机化不足以识别，要么是因为治疗未随机化且治疗与结果关系可能存在未测量的混杂因素。我们针对这些情形开发了敏感性分析技术。我们描述了几种针对传染性效应的敏感性分析技术，在疫苗试验中，该效应捕捉一个人接种疫苗对保护另一个人免受感染的影响，即使第一个人已被感染。我们还开发了两种存在未测量混杂因素时因果效应的敏感性分析技术，它们推广了不存在干扰时的类似技术。对这两种未测量混杂因素的技术进行了比较和对比。
