Stepped-wedge designs are increasingly used in randomized experiments to accommodate logistical and ethical constraints by staggering treatment roll-out over time. Despite their popularity, existing analytical methods largely rely on parametric models with linear covariate adjustment and prespecified correlation structures, which may limit achievable precision in practice. We propose a new class of estimators for the causal average treatment effect in stepped-wedge designs that optimizes precision through flexible, machine-learning-based covariate adjustment to capture complex outcome-covariate relationships, together with quadratic inference functions to adaptively learn the correlation structure. We establish consistency and asymptotic normality under mild conditions requiring only $L_2$ convergence of nuisance estimators, even under model misspecification, and characterize when the estimator attains the minimal asymptotic variance. Moreover, we prove that the proposed estimator never reduces efficiency relative to an independence working correlation. The proposed method further accommodates treatment-effect heterogeneity across both exposure duration and calendar time. Finally, we demonstrate our methods through simulation studies and reanalyses of two empirical studies that differ substantially in research area and key design parameters.

翻译：阶梯楔形设计在随机化实验中日益普及，其通过随时间错开处理实施来适应后勤与伦理约束。尽管应用广泛，现有分析方法主要依赖于采用线性协变量调整与预设相关结构的参数模型，这可能限制实践中可达到的精度。本文针对阶梯楔形设计中的因果平均处理效应，提出一类新的估计量，通过基于机器学习的灵活协变量调整来捕捉复杂的结局-协变量关系，并结合二次推断函数自适应学习相关结构，从而实现精度优化。我们在仅需干扰参数估计量满足$L_2$收敛的温和条件下，建立了估计量的一致性与渐近正态性（即使在模型误设情况下），并刻画了估计量达到最小渐近方差的场景。此外，我们证明所提估计量相对于独立工作相关结构永远不会降低效率。该方法进一步适应了处理效应在暴露时长与日历时间两个维度上的异质性。最后，我们通过模拟研究及对两个在研究领域与关键设计参数上存在显著差异的实证研究的再分析，展示了所提方法的实际应用。