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

arXiv 2605.31443 ↗First posted 29 May 2026stat.ME cs.LG econ.EMcited by 0

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Abstract (arXiv)

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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References · 28

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16 more
  1. 2020Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimationcited by 27
  2. 2019On the bias, risk and consistency of sample means in multi-armed banditscited by 24
  3. 2020Nonconvex sparse regularization for deep neural networks and its optimalitycited by 21
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  8. 2025Modeling Time-evolving Causality over Data Streamscited by 6
  9. 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationcited by 6
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  11. 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductioncited by 5
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  16. 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliancecited by 2

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