# Double Machine Learning for Static Panel Models with Fixed Effects

arXiv: https://arxiv.org/abs/2312.08174 · HTML: https://mlsanity.com/paper/2312.08174/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Structural Nested Models and G-estimation: The Partially Realized Promise](https://mlsanity.com/paper/1503.01589/) (2015)
- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R](https://mlsanity.com/paper/2103.09603/) (2021)
- [Inference in High Dimensional Panel Models with an Application to Gun Control](https://mlsanity.com/paper/1411.6507/) (2014)
- [Double machine learning for sample selection models](https://mlsanity.com/paper/2012.00745/) (2020)
- [All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples](https://mlsanity.com/paper/2211.10310/) (2022)
- [The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation](https://mlsanity.com/paper/2303.01412/) (2023)
- [Modified Causal Forests for Estimating Heterogeneous Causal Effects](https://mlsanity.com/paper/1812.09487/) (2018)
- [Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice](https://mlsanity.com/paper/2310.18212/) (2023)
- [Uniform Inference in High-dimensional Dynamic Panel Data Models](https://mlsanity.com/paper/1501.00478/) (2015)
- [The Fragility of Sparsity](https://mlsanity.com/paper/2311.02299/) (2023)

## Built on by (most cited first, 4 of 4)

- [Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions](https://mlsanity.com/paper/2409.01266/) (2024)
- [Causal Machine Learning for Moderation Effects](https://mlsanity.com/paper/2401.08290/) (2024)
- [Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation](https://mlsanity.com/paper/2508.20335/) (2025)
- [xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R](https://mlsanity.com/paper/2512.15965/) (2025)

## Abstract (arXiv)

Recent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. In this paper, we develop novel double machine learning (DML) procedures for panel data in which these algorithms are used to approximate high-dimensional and nonlinear nuisance functions of the covariates. Our new procedures are extensions of the well-known correlated random effects, within-group and first-difference estimators from linear to nonlinear panel models, specifically, Robinson (1988)'s partially linear regression model with fixed effects and unspecified nonlinear confounding. Our simulation study assesses the performance of these procedures using different machine learning algorithms. We use our procedures to re-estimate the impact of minimum wage on voting behaviour in the UK. From our results, we recommend the use of first-differencing because it imposes the fewest constraints on the distribution of the fixed effects, and an ensemble learning strategy to ensure optimum estimator accuracy.