Abstract

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-square 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.

摘要

本文为集群、分块随机对照试验 (RCT) 开发了基于设计的比率估计器，并将其应用于联邦政府资助的、以学校为基础的 RCT 测试行为健康干预的效果。我们考虑平均治疗效果 (ATE) 的有限总体加权最小二乘估计量，允许一般加权方案和协变量。我们考虑具有逐块处理状态交互的模型以及仅具有块指示器的受限模型。我们为每个块规范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集群鲁棒标准误差估计器共享特征的简单方差估计器。模拟表明，基于设计的 ATE 估计器产生标称拒绝率，标准误差接近真实值，即使集群很少。