Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
This work provides a method for researchers and practitioners to understand the timing and duration of treatment effects in randomized experiments, moving beyond simple average effects.
This paper introduces a regression-adjustment framework to estimate longitudinal treatment effects in randomized experiments under static regimes. The method models dynamic trajectories of intermediate outcomes and evolving post-treatment covariates using transition kernels, leading to more powerful statistical inference.
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.