论文标题
一类实验设计的渐近效率界限
Asymptotic Efficiency Bounds for a Class of Experimental Designs
论文作者
论文摘要
我们考虑了一个实验设计设置，在该设置中，在无限人群中依次采样后，将单位分配给治疗。我们得出渐近效率界限，这些范围适用于任何将处理作为协变量和过去结果数据的（可能是随机的）功能的数据，包括对协变量和自适应设计的分层。为了估计二进制治疗的平均治疗效果，我们的结果表明，相对于实现Hahn（1998）在实验设计中绑定的估计量相对于估计数量而言，无需进一步的一阶渐近效率提高，其中选择了倾向得分以最大程度地减少这种结合。我们的结果还适用于具有多种治疗方法的设置，并可能在治疗方面受到限制，以及对单个结果的协变量采样。
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.
