We consider the problem of efficient inference of the Average Treatment
Effect in a sequential experiment where the policy governing the assignment of
subjects to treatment or control can change over time. We first provide a
central limit theorem for the Adaptive Augmented Inverse-Probability Weighted
estimator, which is semiparametric efficient, under weaker assumptions than
those previously made in the literature. This central limit theorem enables
efficient inference at fixed sample sizes. We then consider a sequential
inference setting, deriving both asymptotic and nonasymptotic confidence
sequences that are considerably tighter than previous methods. These
anytime-valid methods enable inference under data-dependent stopping times
(sample sizes). Additionally, we use propensity score truncation techniques
from the recent off-policy estimation literature to reduce the finite sample
variance of our estimator without affecting the asymptotic variance. Empirical
results demonstrate that our methods yield narrower confidence sequences than
those previously developed in the literature while maintaining time-uniform
error control.

在顺序实验中，通过使用自适应增广逆概率加权估计量和倾向得分截断技术，我们提出了一种有效的平均处理效应推断方法，能够在变动的样本规模下进行推断并具有较窄的置信区间。