# Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation

arXiv: https://arxiv.org/abs/2002.07285 · HTML: https://mlsanity.com/paper/2002.07285/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 27 of 27)

- [Model-agnostic meta-learners for estimating heterogeneous treatment effects over time](https://mlsanity.com/paper/2407.05287/) (2024)
- [Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders](https://mlsanity.com/paper/2302.00662/) (2023)
- [Estimating the Long-Term Effects of Novel Treatments](https://mlsanity.com/paper/2103.08390/) (2021)
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- [Distributed Double Machine Learning with a Serverless Architecture](https://mlsanity.com/paper/2101.04025/) (2021)
- [Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves](https://mlsanity.com/paper/2111.03950/) (2021)
- [Coordinated Double Machine Learning](https://mlsanity.com/paper/2206.00885/) (2022)
- [Inference on Optimal Dynamic Policies via Softmax Approximation](https://mlsanity.com/paper/2303.04416/) (2023)
- [Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes](https://mlsanity.com/paper/2404.00099/) (2024)
- [Overlap-weighted orthogonal meta-learner for treatment effect estimation over time](https://mlsanity.com/paper/2510.19643/) (2025)
- [Consolidating Rewarded Perturbations for LLM Post-Training](https://mlsanity.com/paper/2605.31494/) (2026)
- [Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model](https://mlsanity.com/paper/2405.11377/) (2024)
- [Semiparametric inference for impulse response functions using double/debiased machine learning](https://mlsanity.com/paper/2411.10009/) (2024)
- [DeepBlip: Estimating Conditional Average Treatment Effects Over Time](https://mlsanity.com/paper/2511.14545/) (2025)
- [Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data](https://mlsanity.com/paper/2310.10559/) (2023)
- [Dynamic covariate balancing: estimating treatment effects over time with potential local projections](https://mlsanity.com/paper/2103.01280/) (2021)
- [Robust Orthogonal Machine Learning of Treatment Effects](https://mlsanity.com/paper/2103.11869/) (2021)
- [Average Adjusted Association: Efficient Estimation with High Dimensional Confounders](https://mlsanity.com/paper/2205.14048/) (2022)
- [A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation](https://mlsanity.com/paper/2306.16297/) (2023)
- [Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data](https://mlsanity.com/paper/2411.14665/) (2024)
- [Latent Variable Modeling for Robust Causal Effect Estimation](https://mlsanity.com/paper/2508.20259/) (2025)
- [An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes](https://mlsanity.com/paper/2509.26429/) (2025)
- [xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R](https://mlsanity.com/paper/2512.15965/) (2025)
- [Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation](https://mlsanity.com/paper/2602.12379/) (2026)
- [Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach](https://mlsanity.com/paper/2603.02359/) (2026)
- [Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments](https://mlsanity.com/paper/2605.31443/) (2026)

## Abstract (arXiv)

We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes or the state of the treated unit. We propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments, which can be viewed as a Neyman orthogonal (locally robust) cross-fitted version of g-estimation in the dynamic treatment regime. Our method applies to a general class of non-linear dynamic treatment models known as Structural Nested Mean Models and allows the use of machine learning methods to control for potentially high dimensional state variables, subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the structural parameters of interest. These structural parameters can be used for off-policy evaluation of any target dynamic policy at parametric rates, subject to semi-parametric restrictions on the data generating process. Our work is based on a recursive peeling process, typical in g-estimation, and formulates a strongly convex objective at each stage, which allows us to extend the g-estimation framework in multiple directions: i) to provide finite sample guarantees, ii) to estimate non-linear effect heterogeneity with respect to fixed unit characteristics, within arbitrary function spaces, enabling a dynamic analogue of the RLearner algorithm for heterogeneous effects, iii) to allow for high-dimensional sparse parameterizations of the target structural functions, enabling automated model selection via a recursive lasso algorithm. We also provide guarantees for data stemming from a single treated unit over a long horizon and under stationarity conditions.