{"ID":2828232,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15965","arxiv_id":"2512.15965","title":"xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R","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.","short_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-dimensiona...","url_abs":"https://arxiv.org/abs/2512.15965","url_pdf":"https://arxiv.org/pdf/2512.15965v1","authors":"[\"Annalivia Polselli\"]","published":"2025-12-17T20:48:40Z","proceeding":"econ.EM","tasks":"[\"econ.EM\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}