随机化区组实验与重随机实验中基于设计的 Lasso 调整理论
Design-based theory for Lasso adjustment in randomized block experiments and rerandomized experiments
AI总结：
提出基于Lasso的回归调整方法，用于在高维协变量随机化区组与重随机实验中高效估计平均处理效应，推导了渐近性质并验证了其优势。
摘要：
区组化是重随机的一种特例，通常在随机化实验的设计阶段实施，以平衡基线协变量。本研究提出了一种基于最小绝对收缩与选择算子（Lasso）的回归调整方法，用于在具有高维协变量的随机化区组实验中高效估计平均处理效应。我们推导了所提估计量的渐近性质，并概述了该估计量比未调整估计量更有效的条件。我们提供了一个保守的方差估计量以促进有效推断。我们的框架允许某些区组中仅有一个处理或对照单元，以及各区组间异质的倾向得分，从而将配对实验和精细分层实验作为特例纳入其中。我们进一步兼容了重随机实验以及区组化与重随机化的结合。此外，我们的分析允许区组数量和区组规模均趋于无穷大，以及各区组间的异质处理效应，且无需假设真实的结局数据生成模型。模拟研究和两项真实数据分析证明了所提方法的优势。
英文摘要：
Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regression adjustment method based on the least absolute shrinkage and selection operator (Lasso) to efficiently estimate the average treatment effect in randomized block experiments with high-dimensional covariates. We derive the asymptotic properties of the proposed estimator and outline the conditions under which this estimator is more efficient than the unadjusted one. We provide a conservative variance estimator to facilitate valid inferences. Our framework allows one treated or control unit in some blocks and heterogeneous propensity scores across blocks, thus including paired experiments and finely stratified experiments as special cases. We further accommodate rerandomized experiments and a combination of blocking and rerandomization. Moreover, our analysis allows both the number of blocks and block sizes to tend to infinity, as well as heterogeneous treatment effects across blocks without assuming a true outcome data-generating model. Simulation studies and two real-data analyses demonstrate the advantages of the proposed method.
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