论文标题
有效的自适应实验设计，用于平均治疗效果估计
Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
论文作者
论文摘要
我们研究如何使用自适应实验有效地估计平均治疗效果（ATE）。在自适应实验中，实验者将基于过去数据的治疗分配概率进行依次分配给实验单元。我们首先定义有效的治疗分配概率，该概率最大程度地减少了用于ATE估计的半摩托效率。我们提出的实验设计估算，并使用有效的治疗分配概率来分配治疗。在拟议的设计结束时，实验者使用新提出的自适应增强反概率加权（A2IPW）估计器估算ATE。我们表明，使用拟议设计的数据，A2IPW估计器的渐近方差达到了最小化的半参数效率结合。我们还分析了估计量的有限样本特性，并开发非参数和非反应置信区间，这些间隔在提议的设计的任何一轮中都是有效的。这些任何时间有效的置信区间使我们能够进行速率最佳的顺序假设测试，从而可以尽早停止和减少必要的样本量。
We study how to efficiently estimate average treatment effects (ATEs) using adaptive experiments. In adaptive experiments, experimenters sequentially assign treatments to experimental units while updating treatment assignment probabilities based on past data. We start by defining the efficient treatment-assignment probability, which minimizes the semiparametric efficiency bound for ATE estimation. Our proposed experimental design estimates and uses the efficient treatment-assignment probability to assign treatments. At the end of the proposed design, the experimenter estimates the ATE using a newly proposed Adaptive Augmented Inverse Probability Weighting (A2IPW) estimator. We show that the asymptotic variance of the A2IPW estimator using data from the proposed design achieves the minimized semiparametric efficiency bound. We also analyze the estimator's finite-sample properties and develop nonparametric and nonasymptotic confidence intervals that are valid at any round of the proposed design. These anytime valid confidence intervals allow us to conduct rate-optimal sequential hypothesis testing, allowing for early stopping and reducing necessary sample size.
