Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments

ArXi:2605.31443v1 Announce Type: cross 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.