We consider the problem of detecting a Return to Baseline (RtB) in high-frequency monitoring data preceding and following an intervention, where the aim is to identify the time at which the data-generating distribution realigns with its pre-intervention distribution. We propose a sequential, distribution-free testing procedure that does not rely on specifying a null model and provides anytime-valid error control. The method relies on ideas from universal inference to define a discrepancy measure that is aggregated into a non-negative super-martingale, and is then empirically cal- ibrated to form an e-process. The calibration is performed using the baseline data, and is thus subject-specific. We establish finite-sample bounds for the calibration error (under a flexible non-parametric assumption), discuss the impact of tuning parameters and computational complexity, and illustrate through simulations and a clinical case study that the procedure accurately detects RtB from monitoring data.

翻译：我们研究了在高频监测数据中检测干预前后“基线回归”（Return to Baseline, RtB）的问题，旨在识别数据生成分布何时重新对齐其干预前分布。我们提出了一种序贯、无分布假设的检验程序，该方法无需指定零模型，并能提供任意时刻有效的误差控制。该技术借鉴通用推断的思想，定义一种差异度量并将其聚合为非负超鞅，进而通过经验校准构建为e过程。校准过程基于基线数据完成，因此具有个体特异性。我们在灵活的非参数假设下建立了校准误差的有限样本界，讨论了调参参数的影响及计算复杂度，并通过模拟实验和临床案例研究表明，该程序能够从监测数据中准确检测RtB。