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

arXiv: https://arxiv.org/abs/2605.31443 · HTML: https://mlsanity.com/paper/2605.31443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 81 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 28 of 28)

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- [Reconstructing regime-dependent causal relationships from observational time series](https://mlsanity.com/paper/2007.00267/) (2020)
- [Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation](https://mlsanity.com/paper/2002.07285/) (2020)
- [On the bias, risk and consistency of sample means in multi-armed bandits](https://mlsanity.com/paper/1902.00746/) (2019)
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- [Model-agnostic meta-learners for estimating heterogeneous treatment effects over time](https://mlsanity.com/paper/2407.05287/) (2024)
- [Machine Learning for Variance Reduction in Online Experiments](https://mlsanity.com/paper/2106.07263/) (2021)
- [The Econometrics of Randomized Experiments](https://mlsanity.com/paper/1607.00698/) (2016)
- [High-dimensional regression adjustments in randomized experiments](https://mlsanity.com/paper/1607.06801/) (2016)
- [SpaceTime: Causal Discovery from Non-Stationary Time Series](https://mlsanity.com/paper/2501.10235/) (2025)
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- [An Introduction to Double/Debiased Machine Learning](https://mlsanity.com/paper/2504.08324/) (2025)
- [On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization](https://mlsanity.com/paper/2506.05945/) (2025)
- [Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials](https://mlsanity.com/paper/2407.14074/) (2024)
- [Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction](https://mlsanity.com/paper/2407.16037/) (2024)
- [Leveraging covariate adjustments at scale in online A/B testing](https://mlsanity.com/paper/2305.01109/) (2023)
- [High-dimensional Inference for Dynamic Treatment Effects](https://mlsanity.com/paper/2110.04924/) (2021)
- [Beyond the Average: Distributional Causal Inference under Imperfect Compliance](https://mlsanity.com/paper/2509.15594/) (2025)

## 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.