# xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R

arXiv: https://arxiv.org/abs/2512.15965 · HTML: https://mlsanity.com/paper/2512.15965/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation](https://mlsanity.com/paper/2002.07285/) (2020)
- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R](https://mlsanity.com/paper/2103.09603/) (2021)
- [Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study](https://mlsanity.com/paper/2402.04674/) (2024)
- [Double machine learning for sample selection models](https://mlsanity.com/paper/2012.00745/) (2020)
- [Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India](https://mlsanity.com/paper/1712.04802/) (2017)
- [Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice](https://mlsanity.com/paper/2310.18212/) (2023)
- [Double Machine Learning for Static Panel Models with Fixed Effects](https://mlsanity.com/paper/2312.08174/) (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)
- [Local Projection Inference in High Dimensions](https://mlsanity.com/paper/2209.03218/) (2022)
- [Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning](https://mlsanity.com/paper/2410.21105/) (2024)
- [Weak instrumental variables due to ignored nonlinearities in panel data: A Super Learner Control Function estimator](https://mlsanity.com/paper/2504.03228/) (2025)

## Abstract (arXiv)

The double machine learning (DML) method combines the predictive power of machine learning with statistical estimation to conduct inference about the structural parameter of interest. This paper presents the R package `xtdml`, which implements DML methods for partially linear panel regression models with low-dimensional fixed effects, high-dimensional confounding variables, proposed by Clarke and Polselli (2025). The package provides functionalities to: (a) learn nuisance functions with machine learning algorithms from the `mlr3` ecosystem, (b) handle unobserved individual heterogeneity choosing among first-difference transformation, within-group transformation, and correlated random effects, (c) transform the covariates with min-max normalization and polynomial expansion to improve learning performance. We showcase the use of `xtdml` with both simulated and real longitudinal data.