标题： 当治疗影响服务人群时对聚类随机对照试验的依从者平均因果效应的估计 显示英文标题

标题： Estimating Complier Average Causal Effects for Clustered RCTs When the Treatment Affects the Service Population

摘要： 随机对照试验有时测试旨在改善针对在随机化后确定的个体子集的现有服务的干预措施。 因此，治疗可能会影响服务接受者和所提供服务的组成。 在这种偏差下，使用服务接受者和非接受者数据的意向性治疗估计可能难以解释。 本文开发了因果估计量和逆概率加权（IPW）估计量，用于这些情况下的遵守者群体，采用了一种广义估计方程方法，该方法调整了IPW权重估计误差的标准误差。 虽然我们的重点是更一般的聚类随机对照试验，但这些方法也适用于（减少到）非聚类随机对照试验。 模拟显示，在假设的识别条件下，估计量实现了名义上的置信区间覆盖率。 一个实证应用使用一项大规模随机对照试验的数据来演示这些方法，该试验测试早期儿童服务对儿童认知发展评分的影响。 显示英文摘要

摘要： 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.