标题： 渐近效率界限对于一类实验设计 显示英文标题

标题： Asymptotic Efficiency Bounds for a Class of Experimental Designs

摘要： 我们考虑一个实验设计环境，在这个环境中，单位是从无限总体中依次抽样后被分配到处理组的。 我们推导了渐近效率界限，这些界限适用于任何将处理作为协变量和过去结果数据的函数（可能是随机化的）进行分配的实验数据，包括基于协变量的分层和自适应设计。 对于估计二元处理的平均处理效应，我们的结果表明，在一个倾向得分被选择以最小化该界限的实验设计中，没有进一步的一阶渐近效率改进是可能的，相对于达到Hahn（1998）界限的估计量而言。 我们的结果也适用于存在多个处理且可能对处理有约束的情况，以及基于协变量的单个结果的抽样设置。 显示英文摘要

摘要： We consider an experimental design setting in which units are assigned to treatment after being sampled sequentially from an infinite population. We derive asymptotic efficiency bounds that apply to data from any experiment that assigns treatment as a (possibly randomized) function of covariates and past outcome data, including stratification on covariates and adaptive designs. For estimating the average treatment effect of a binary treatment, our results show that no further first order asymptotic efficiency improvement is possible relative to an estimator that achieves the Hahn (1998) bound in an experimental design where the propensity score is chosen to minimize this bound. Our results also apply to settings with multiple treatments with possible constraints on treatment, as well as covariate based sampling of a single outcome.