Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs

• 成果类型: Article
• 署名作者: Schochet, Peter Z.; Pashley, Nicole E.; Miratrix, Luke W.; Kautz, Tim
• 署名单位: Mathematica; Rutgers University System; Rutgers University New Brunswick; Harvard University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1906685
• 发表日期: 2022
• 页码: 2135-2146
• 关键词: REGRESSION ADJUSTMENTS; randomized experiments; matrix estimator; neyman model; causal; statistics; variance

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-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.