Hudgens Michael G, Halloran M Elizabeth
Michael G. Hudgens is Research Associate Professor, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599 (E-mail:
J Am Stat Assoc. 2008 Jun;103(482):832-842. doi: 10.1198/016214508000000292.
A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this article, we consider a population of groups of individuals where interference is possible between individuals within the same group. We propose estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting. Relations among the estimands are established; for example, the total causal effect is shown to equal the sum of direct and indirect causal effects. Using an experimental design with a two-stage randomization procedure (first at the group level, then at the individual level within groups), unbiased estimators of the proposed estimands are presented. Variances of the estimators are also developed. The methodology is illustrated in two different settings where interference is likely: assessing causal effects of housing vouchers and of vaccines.
因果推断中通常做出的一个基本假设是个体（或单元）之间不存在干扰；也就是说，假定一个个体的潜在结果不受其他个体治疗分配的影响。然而，在许多情况下，这个假设显然不成立。例如，在传染病的相关事件中，一个人是否被感染取决于人群中其他接种疫苗的人。在本文中，我们考虑一个个体组的总体，其中同一组内的个体之间可能存在干扰。我们针对这种情况下治疗策略的直接、间接、总体和全面因果效应提出了估计量。建立了估计量之间的关系；例如，总体因果效应被证明等于直接因果效应与间接因果效应之和。使用一种具有两阶段随机化程序（首先在组级别，然后在组内个体级别）的实验设计，给出了所提出估计量的无偏估计量。还推导了估计量的方差。该方法在两个可能存在干扰的不同场景中进行了说明：评估住房券和疫苗的因果效应。
