论文标题
在协变量存在下有限种群中治疗效应估计量方差的急剧界限
Sharp bounds for variance of treatment effect estimators in the finite population in the presence of covariates
论文作者
论文摘要
在一个完全随机的实验中，有限种群中的治疗效应估计器的差异通常是无法识别的，因此无法估计。尽管文献中已经建立了一些差异的界限，但在协变量的存在下很少有这些差异。 在本文中，在完全随机的实验中考虑了均值估计器和WALD估计量，分别具有完美的依从性和不合规性。当有协变量可用时，建立了这两个估计量方差的急剧界限。 此外，获得了此类界限的一致估计器，可用于缩短置信区间并提高测试功能。置信区间是基于上限的一致估计器来构建的，上限的覆盖率均匀地保证其覆盖率。 进行了模拟以评估所提出的方法。还通过两个实际数据分析来说明所提出的方法。
In a completely randomized experiment, the variances of treatment effect estimators in the finite population are usually not identifiable and hence not estimable. Although some estimable bounds of the variances have been established in the literature, few of them are derived in the presence of covariates. In this paper, the difference-in-means estimator and the Wald estimator are considered in the completely randomized experiment with perfect compliance and noncompliance, respectively. Sharp bounds for the variances of these two estimators are established when covariates are available. Furthermore, consistent estimators for such bounds are obtained, which can be used to shorten the confidence intervals and improve the power of tests. Confidence intervals are constructed based on the consistent estimators of the upper bounds, whose coverage rates are uniformly asymptotically guaranteed. Simulations were conducted to evaluate the proposed methods. The proposed methods are also illustrated with two real data analyses.
