Papadogeorgou Georgia, Mealli Fabrizia, Zigler Corwin M
Department of Statistical Science, Duke University, Durham, North Carolina.
Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy.
Biometrics. 2019 Sep;75(3):778-787. doi: 10.1111/biom.13049. Epub 2019 Apr 13.
Interference arises when an individual's potential outcome depends on the individual treatment level, but also on the treatment level of others. A common assumption in the causal inference literature in the presence of interference is partial interference, implying that the population can be partitioned in clusters of individuals whose potential outcomes only depend on the treatment of units within the same cluster. Previous literature has defined average potential outcomes under counterfactual scenarios where treatments are randomly allocated to units within a cluster. However, within clusters there may be units that are more or less likely to receive treatment based on covariates or neighbors' treatment. We define new estimands that describe average potential outcomes for realistic counterfactual treatment allocation programs, extending existing estimands to take into consideration the units' covariates and dependence between units' treatment assignment. We further propose entirely new estimands for population-level interventions over the collection of clusters, which correspond in the motivating setting to regulations at the federal (vs. cluster or regional) level. We discuss these estimands, propose unbiased estimators and derive asymptotic results as the number of clusters grows. For a small number of observed clusters, a bootstrap approach for confidence intervals is proposed. Finally, we estimate effects in a comparative effectiveness study of power plant emission reduction technologies on ambient ozone pollution.
当个体的潜在结果不仅取决于个体的治疗水平，还取决于其他个体的治疗水平时，就会产生干扰。在存在干扰的因果推断文献中，一个常见的假设是部分干扰，这意味着总体可以被划分为个体集群，其潜在结果仅取决于同一集群内个体的治疗。先前的文献已经定义了在反事实情景下的平均潜在结果，即在集群内将治疗随机分配给个体。然而，在集群内，可能存在一些个体，基于协变量或邻居的治疗，它们接受治疗的可能性或多或少。我们定义了新的估计量，用于描述现实反事实治疗分配方案的平均潜在结果，扩展了现有估计量，以考虑个体的协变量以及个体治疗分配之间的依赖性。我们进一步针对集群集合提出了全新的总体水平干预估计量，在激励设定中，这对应于联邦（相对于集群或区域）层面的法规。我们讨论了这些估计量，提出了无偏估计器，并推导了随着集群数量增加的渐近结果。对于少量观察到的集群，我们提出了一种用于置信区间的自助法。最后，我们在一项关于发电厂减排技术对环境臭氧污染的比较效果研究中估计了效应。
