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

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  • Annalivia Polselli

Abstract

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.

Suggested Citation

  • Annalivia Polselli, 2025. "xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R," Papers 2512.15965, arXiv.org.
    • Handle: RePEc:arx:papers:2512.15965
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    References listed on IDEAS

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    1. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
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