xtdml: Double Machine Learning for Static Panel Models with Fixed
Effects
Implementation of partially linear panel regression (PLPR) models with high-dimensional confounding variables and exogenous treatment variable within the double machine learning framework. It allows the estimation of the structural parameter (treatment effect) in static panel data models with fixed effects using panel data approaches established in Clarke and Polselli (2025) <doi:10.1093/ectj/utaf011>. 'xtdml' is built on the object-oriented 'DoubleML' (Bach et al., 2024) <doi:10.18637/jss.v108.i03> using the 'mlr3' ecosystem.
| Version: |
0.1.5 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
R6 (≥ 2.4.1), data.table (≥ 1.12.8), mlr3 (≥ 0.5.0), mlr3tuning (≥ 0.3.0), mlr3learners (≥ 0.3.0), mlr3misc, mvtnorm, utils, clusterGeneration, readstata13, magrittr, dplyr, stats, MLmetrics, checkmate |
| Suggests: |
rpart, mlr3pipelines |
| Published: |
2025-09-08 |
| DOI: |
10.32614/CRAN.package.xtdml |
| Author: |
Annalivia Polselli
 [aut, cre] |
| Maintainer: |
Annalivia Polselli <apolselli.econ at gmail.com> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
xtdml results |
Documentation:
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