基于设计的聚类、区组化 RCT 的比率估计量与中心极限定理
Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
AI总结：
本文为聚类、区组化 RCT 提出了基于设计的比率估计量，证明了新的有限总体中心极限定理，并通过模拟验证了该 ATE 估计量在聚类数较少时仍具稳健性。
摘要：
本文针对聚类、区组化随机对照试验（RCT）开发了基于设计的比率估计量，并应用于一项测试行为健康干预效果的联邦资助的基于学校的 RCT。我们考虑了平均处理效应（ATE）的有限总体加权最小二乘估计量，允许一般的加权方案和协变量。我们考虑了带有区组与处理状态交互项的模型，以及仅带有区组指示变量的受限模型。我们为每种区组设定证明了新的有限总体中心极限定理。我们还讨论了与常用的聚类稳健标准误估计量具有共同特征的简单方差估计量。模拟表明，即使聚类数量很少，基于设计的 ATE 估计量也能产生名义拒绝率，且标准误接近真实值。
英文摘要：
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.
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