Repeated sampling of different individuals within the same clusters to improve precision of longitudinal estimators: the DISC design
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AI 根据所提供的论文材料归类：摘要研究固定簇的纵向重复抽样，并比较 DID 和双重稳健 DID，直接涉及面板设计与因果效应估计。
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固定簇内重复抽取不同个体以提高纵向估计精度：DISC 设计
- 研究问题
- 无法追踪同一个体时，能否通过固定基线抽中的簇、各期重新抽取簇内个体，提高纵向及双重差分估计的精度？
- 方法与思路
- 作者提出介于队列与重复截面之间的 DISC 抽样设计，推导方差比较，并以模拟和南非家庭调查展示普通及双重稳健 DID。
- 材料陈述的贡献
- 摘要称，存在簇效应时 DISC 可显著降低相对重复截面设计的方差，且模拟显示普通与双重稳健 DID 均有精度收益。
- 阅读关注点
- 应核对重复截面基准是否重新抽簇、簇效应的时间稳定性和样本规模约束，并区分抽样精度收益与因果识别条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要中的方差比依赖特定设计，未说明所有条件，不能据此保证任意场景改善。
原文摘要
Background: Longitudinal studies often involve repeated cluster sampling of a population at multiple time points, such as in difference-in-differences (DID) studies. Although cohort designs typically lead to more efficient estimators relative to repeated cross-sectional (RCS) designs, they are often impractical. Methods: We describe the DISC (Different Individuals, Same Clusters) design, a sampling scheme that improves the precision of estimators in these settings. The DISC design represents a hybrid between a cohort and an RCS design, in which the researcher takes a single sample of clusters at baseline, but takes different cross-sectional samples of individuals within clusters at each time point. Results: We show analytically that the DISC design yields DID estimators with much higher precision relative to an RCS design, particularly if cluster effects are present. For example, for a design with two surveys, 40 clusters, and 25 individuals per cluster, the variance of a commonly-used DID treatment effect estimator is 2.3 times higher in the RCS design for an intraclass correlation coefficient (ICC) of 0.05 and 3.8 times higher for an ICC of 0.1. We also present results of a simulation study comparing the RCS and DISC designs, using both a simple DID estimator and a more complex doubly-robust DID (DRDID) estimator that leverages covariate information, and show gains in precision for both estimators when using the DISC design. Additionally, we illustrate DISC sampling using a household survey dataset from South Africa. Conclusions: Use of the DISC design can result in estimators that have substantially lower variance than the analogous estimator resulting from an RCS design.