Adaptive experimental designs have gained increasing attention across a range of domains. In this paper, we propose a new methodological framework, surrogate-leveraged online adaptive causal inference (SLOACI), which integrates predictive surrogate outcomes into adaptive designs to enhance efficiency. For downstream analysis, we construct the adaptive augmented inverse probability weighting estimator for the average treatment effect using collected data. Our procedure remains robust even when surrogates are noisy or weak. We provide a comprehensive theoretical foundation for SLOACI. Under the asymptotic regime, we show that the proposed estimator attains the semiparametric efficiency bound. From a non-asymptotic perspective, we derive a regret bound to provide practical insights. We also develop a toolbox of sequential testing procedures that accommodates both asymptotic and non-asymptotic regimes, allowing experimenters to choose the perspective that best aligns with their practical needs. Extensive simulations and a synthetic case study are conducted to showcase the superior finite-sample performance of our method.

翻译：自适应实验设计在多个领域日益受到关注。本文提出一种新的方法框架——基于代理变量的在线自适应因果推断（SLOACI），该框架将预测性代理结果整合到自适应设计中以提高效率。针对下游分析，我们利用收集的数据构建了自适应增强逆概率加权估计量以估计平均处理效应。即使代理变量存在噪声或较弱，我们的方法仍保持稳健性。我们为SLOACI提供了完整的理论基础：在渐近框架下，证明了所提估计量达到半参数效率下界；从非渐近视角出发，推导了遗憾界以提供实践指导。我们还开发了一套适用于渐近与非渐近框架的序贯检验工具箱，使实验者可根据实际需求选择合适视角。通过大量模拟实验与合成案例研究，验证了本方法在有限样本下的优越性能。