Rejective Sampling, Rerandomization and Regression Adjustment in Survey Experiments
调查实验中的拒绝式抽样、再随机化与回归调整AI 解读 ↓
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调查实验中的拒绝式抽样、再随机化与回归调整
- 研究问题
- 如何同时减少随机调查实验中样本与目标总体之间、处理组与对照组之间的协变量失衡，并提高处理效应估计精度？
- 方法与思路
- 作者提出在抽样和处理分配两个阶段实施再随机化，建立相应渐近理论，并通过协变量调整处理再随机化之后残留的失衡。
- 材料陈述的贡献
- 摘要称，两阶段设计改善协变量平衡、提高估计精度并缩短大样本区间；调整可进一步改善精度，框架兼容各阶段不同的协变量关系。
- 阅读关注点
- 应核对两阶段接受规则、抽样与分配机制、渐近条件，以及分析阶段协变量调整如何影响方差估计和置信区间覆盖。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出设计约束与完整条件，其大样本改进不能直接视为有限样本保证。
原文摘要
Classical randomized experiments, equipped with randomization-based inference, provide assumption-free inference for treatment effects. They have been the gold standard for drawing causal inference and provide excellent internal validity. However, they have also been criticized for questionable external validity, in the sense that the conclusion may not generalize well to a larger population. The randomized survey experiment is a design tool that can help mitigate this concern, by randomly selecting the experimental units from the target population of interest. However, as pointed out by Morgan and Rubin (2012), chance imbalances often exist in covariate distributions between different treatment groups even under completely randomized experiments. Not surprisingly, such covariate imbalances also occur in randomized survey experiments. Furthermore, the covariate imbalances happen not only between different treatment groups, but also between the sampled experimental units and the overall population of interest. In this paper, we propose a two-stage rerandomization design that can actively avoid undesirable covariate imbalances at both the sampling and treatment assignment stages. We further develop asymptotic theory for rerandomized survey experiments, demonstrating that rerandomization provides better covariate balance, more precise treatment effect estimators, and shorter large-sample confidence intervals. We also propose covariate adjustment to deal with remaining covariate imbalances after rerandomization, showing that it can further improve both the sampling and estimated precision. Our work allows general relationship among covariates at the sampling, treatment assignment and analysis stages, and generalizes both rerandomization in classical randomized experiments (Morgan and Rubin 2012) and rejective sampling in survey sampling (Fuller 2009).