Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
通过协变量转移建模高效估计随机实验中的纵向治疗效应
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…