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

2026-05-29

AI Summary

This study addresses the limitation of traditional regression adjustment methods, which can only estimate average treatment effects and fail to capture the dynamic evolution of treatment effects over time. The authors propose a novel longitudinal treatment effect estimation framework that, for the first time, incorporates time-varying covariate transition mechanisms into regression adjustment. By modeling post-treatment covariate trajectories through transition kernels, the method enables a fine-grained characterization of effect heterogeneity across time. The proposed estimator is theoretically shown to achieve the semiparametric efficiency bound and possesses asymptotic normality. Both simulation studies and empirical analysis using A/B test data from a Japanese streaming platform demonstrate that the approach substantially reduces estimation variance and enhances statistical inference efficiency.

Abstract

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.

Problem

• longitudinal treatment effects
• randomized experiments
• dynamic trajectories
• intermediate outcomes
• post-treatment covariates

Innovation

• regression adjustment
• longitudinal treatment effects
• transition kernels
• semiparametric efficiency
• randomized experiments