Model-assisted analyses of cluster-randomized experiments
整群随机实验的模型辅助分析AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究整群随机处理分配下的平均处理效应及设计本位推断，直接属于实验因果推断。
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整群随机实验的模型辅助分析
- 研究问题
- 整群随机实验中群规模不同时，个体回归、群均值回归与群总量回归分别估计什么，如何选择更有效率的分析与标准误？
- 方法与思路
- 作者采用仅将处理分配视为随机的设计本位框架，比较三类回归、协变量调整及稳健标准误，并扩展至加权平均处理效应。
- 材料陈述的贡献
- 摘要称，群均值回归针对加权效应，个体回归效率非最优；群数较多时群总量回归一致且更有效率，稳健标准误可近似渐近标准误。
- 阅读关注点
- 核对各回归对应的权重与目标效应、群规模及群数的渐近条件、协变量调整形式，以及稳健标准误近似成立的范围。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出完整设计条件，群数较少时的精度与标准误表现不能据此确定。
原文摘要
Cluster-randomized experiments are widely used due to their logistical convenience and policy relevance. To analyze them properly, we must address the fact that the treatment is assigned at the cluster level instead of the individual level. Standard analytic strategies are regressions based on individual data, cluster averages, and cluster totals, which differ when the cluster sizes vary. These methods are often motivated by models with strong and unverifiable assumptions, and the choice among them can be subjective. Without any outcome modeling assumption, we evaluate these regression estimators and the associated robust standard errors from a design-based perspective where only the treatment assignment itself is random and controlled by the experimenter. We demonstrate that regression based on cluster averages targets a weighted average treatment effect, regression based on individual data is suboptimal in terms of efficiency, and regression based on cluster totals is consistent and more efficient with a large number of clusters. We highlight the critical role of covariates in improving estimation efficiency, and illustrate the efficiency gain via both simulation studies and data analysis. Moreover, we show that the robust standard errors are convenient approximations to the true asymptotic standard errors under the design-based perspective. Our theory holds even when the outcome models are misspecified, so it is model-assisted rather than model-based. We also extend the theory to a wider class of weighted average treatment effects.