We present a regression-adjustment framework designed for the estimation of longitudinal treatment effects in randomized experiments under static regimes. While regression-adjustment methods are useful for variance reduction in randomized experiments by using pre-treatment covariates, they usually focus only on average effects, from which we cannot obtain valuable insights into when the effects appear and how long they continue. To address this issue, we consider intermediate outcomes and evolving post-treatment covariates over time, and we represent such dynamic trajectories using transition kernels. Furthermore, we establish the asymptotic normality and the semiparametric efficiency bound for our estimator, enabling more powerful statistical inference. Simulation studies and empirical analysis using A/B test data from a streaming platform in Japan show the practical advantages of our method.

翻译：我们提出一种适用于静态规则下随机实验中纵向治疗效果估计的回归调整框架。尽管回归调整方法通过利用预处理协变量能够有效降低随机实验的方差，但其通常仅关注平均效应，无法揭示效应出现的时机及持续时长等关键信息。为此，我们通过引入中间结果与随时间演变的治疗后协变量，并采用转移核函数表征此类动态轨迹。进一步，我们推导出估计量的渐进正态性与半参数效率界，从而增强统计推断效能。基于日本某流媒体平台A/B测试数据的仿真研究与实证分析，验证了该方法在实际应用中的优势。