论文标题
基于设计的比率估计器和中央限制定理，用于群集，阻塞的RCT
Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
论文作者
论文摘要
本文开发了基于设计的比率估计量，用于群集，阻塞的随机对照试验（RCT），并将其应用于联邦资助的基于学校的RCT，以测试行为健康干预措施的效果。我们考虑有限的人口加权最小二乘估计器以达到平均治疗效果（ATE），从而允许一般的加权方案和协变量。我们考虑具有逐个处理状态相互作用的模型以及仅具有块指标的限制模型。我们证明了每个块规范的新有限人口中心限制定理。我们还讨论了简单的方差估计器，这些方差估计器与常用的群集合格标准误差估计器共享特征。模拟表明，基于设计的ATE估计值也会产生名义排斥率，即使很少有群集，也要在真实误差附近产生标准误差。
This article develops design-based ratio estimators for clustered, blocked randomized controlled trials (RCTs), with an application to a federally funded, school-based RCT testing the effects of behavioral health interventions. We consider finite population weighted least squares estimators for average treatment effects (ATEs), allowing for general weighting schemes and covariates. We consider models with block-by-treatment status interactions as well as restricted models with block indicators only. We prove new finite population central limit theorems for each block specification. We also discuss simple variance estimators that share features with commonly used cluster-robust standard error estimators. Simulations show that the design-based ATE estimator yields nominal rejection rates with standard errors near true ones, even with few clusters.
