# Evaluating (weighted) dynamic treatment effects by double machine learning

arXiv: https://arxiv.org/abs/2012.00370 · HTML: https://mlsanity.com/paper/2012.00370/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [Estimating the Long-Term Effects of Novel Treatments](https://mlsanity.com/paper/2103.08390/) (2021)
- [Semi-parametric inference based on adaptively collected data](https://mlsanity.com/paper/2303.02534/) (2023)
- [Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves](https://mlsanity.com/paper/2111.03950/) (2021)
- [Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning](https://mlsanity.com/paper/2307.01049/) (2023)
- [How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign](https://mlsanity.com/paper/2204.10820/) (2022)
- [Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameters](https://mlsanity.com/paper/2507.07941/) (2025)
- [Inverting estimating equations for causal inference on quantiles](https://mlsanity.com/paper/2401.00987/) (2024)
- [Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions](https://mlsanity.com/paper/2409.01266/) (2024)
- [Dynamic covariate balancing: estimating treatment effects over time with potential local projections](https://mlsanity.com/paper/2103.01280/) (2021)
- [A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation](https://mlsanity.com/paper/2306.16297/) (2023)
- [Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation](https://mlsanity.com/paper/2602.12379/) (2026)

## Abstract (arXiv)

We consider evaluating the causal effects of dynamic treatments, i.e. of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-observables assumption. To this end, we make use of so-called Neyman-orthogonal score functions, which imply the robustness of treatment effect estimation to moderate (local) misspecifications of the dynamic outcome and treatment models. This robustness property permits approximating outcome and treatment models by double machine learning even under high dimensional covariates and is combined with data splitting to prevent overfitting. In addition to effect estimation for the total population, we consider weighted estimation that permits assessing dynamic treatment effects in specific subgroups, e.g. among those treated in the first treatment period. We demonstrate that the estimators are asymptotically normal and √n-consistent under specific regularity conditions and investigate their finite sample properties in a simulation study. Finally, we apply the methods to the Job Corps study in order to assess different sequences of training programs under a large set of covariates.