论文标题
随机实验中的少数情况调整调整
Tyranny-of-the-minority regression adjustment in randomized experiments
论文作者
论文摘要
回归调整被广泛用于分析随机实验，以提高治疗效果的估计效率。本文重新检查了一种称为少数暴政（TOM）的加权回归调整方法，其中少数群体中的单位具有更大的权重。我们证明，即使在完全随机的实验中，这两种回归调整方法在完全随机的实验中渐变相同，但与LIN 2013的回归调整相比，TOM回归调整比Lin 2013的回归调整更强大。此外，我们将TOM回归调整扩展到分层的随机实验，完全随机的调查实验和聚类随机实验。在此类设计下，我们获得了TOM回归调整后的平均治疗效应估计量的基于设计的特性。特别是，我们表明，与未调整的估计量相比，TOM回归调整后的估计量也提高了渐近估计效率，即使回归模型被弄错了，并且在线性调整的估计器类别中也是最佳的。我们还研究了各种异方差标准误差估计器的渐近特性，并为从业者提供建议。仿真研究和实际数据分析表明，TOM回归调整优于现有方法的优势。
Regression adjustment is widely used for the analysis of randomized experiments to improve the estimation efficiency of the treatment effect. This paper reexamines a weighted regression adjustment method termed as tyranny-of-the-minority (ToM), wherein units in the minority group are given greater weights. We demonstrate that the ToM regression adjustment is more robust than Lin 2013's regression adjustment with treatment-covariate interactions, even though these two regression adjustment methods are asymptotically equivalent in completely randomized experiments. Moreover, we extend ToM regression adjustment to stratified randomized experiments, completely randomized survey experiments, and cluster randomized experiments. We obtain design-based properties of the ToM regression-adjusted average treatment effect estimator under such designs. In particular, we show that ToM regression-adjusted estimator improves the asymptotic estimation efficiency compared to the unadjusted estimator even when the regression model is misspecified, and is optimal in the class of linearly adjusted estimators. We also study the asymptotic properties of various heteroscedasticity-robust standard error estimators and provide recommendations for practitioners. Simulation studies and real data analysis demonstrate ToM regression adjustment's superiority over existing methods.
