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

• URL: http://arxiv.org/abs/2512.15965v1
• Date: Wed, 17 Dec 2025 20:48:40 GMT
• Title: xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
• Authors: Annalivia Polselli,
• Abstract summary: The paper presents the R package xtdml, which implements DML methods for partially linear panel regression models.<n>The package provides functionalities to: (a) learn nuisance functions with machine learning algorithms from the mlr3 ecosystem.<n>We showcase the use of xtdml with both simulated and real longitudinal data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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