论文标题
当治疗影响服务人群时，估计聚类RCT的平均因果效应
Estimating Complier Average Causal Effects for Clustered RCTs When the Treatment Affects the Service Population
论文作者
论文摘要
RCT有时测试干预措施，旨在改善针对随机分配后确定的个体子集的现有服务。因此，治疗可能会影响服务接受者的组成和所提供的服务。有了这样的偏见，使用服务接受者的数据和非注册因素可能很难解释意向性治疗的估计。本文使用通用估计方程方法在这些设置中开发了因果估计数和反比概率加权（IPW）估计量，该方法可以调整IPW权重中估计误差的标准误差。尽管我们的重点是更通用的簇RCT，但这些方法也适用于非簇的RCT。模拟表明，在假定的识别条件下，估计器实现了名义置信区间的覆盖范围。经验应用显示了使用大规模RCT的数据测试儿童服务对儿童认知发展评分的影响的方法。
RCTs sometimes test interventions that aim to improve existing services targeted to a subset of individuals identified after randomization. Accordingly, the treatment could affect the composition of service recipients and the offered services. With such bias, intention-to-treat estimates using data on service recipients and nonrecipients may be difficult to interpret. This article develops causal estimands and inverse probability weighting (IPW) estimators for complier populations in these settings, using a generalized estimating equation approach that adjusts the standard errors for estimation error in the IPW weights. While our focus is on more general clustered RCTs, the methods also apply (reduce) to non-clustered RCTs. Simulations show that the estimators achieve nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from a large-scale RCT testing the effects of early childhood services on children's cognitive development scores.
