通过建模协变量动态变化，更精准地追踪长期治疗效应的出现与持续时间。
Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
- 用转移核刻画治疗后协变量随时间演变轨迹。
- 在日系流媒体A/B测试中，显著提升效应估计效率。
- 适合关注治疗效应时序特征的实验设计者使用。
我们提出一种回归调整框架，用于随机实验中静态干预策略下纵向治疗效应的估计。尽管回归调整可通过基线协变量减少方差，但通常仅关注平均效应，难以揭示效应何时出现及持续多久。为此，我们引入中间结果和随时间演化的治疗后协变量，并用转移核表征其动态轨迹。同时，我们建立了估计量的渐近正态性及半参数效率界限，支持更强大的统计推断。模拟研究和日本某流媒体平台的A/B测试数据实证分析表明，该方法具有实际优势。
原文摘要 · Abstract (English)
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.
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