Double Machine Learning for Static Panel Models with Interactive Fixed Effects

Implements partially linear panel regression (PLPR) models with interactive fixed effects, high-dimensional confounding variables, and an exogenous treatment variable within the double machine learning framework. Estimates the structural parameter (treatment effect) in static panel data models with interactive fixed effects using the approach established in Chen et al. (2026) . Builds on the object-oriented package 'DoubleML' (Bach et al., 2024) and 'xtdml' (Polselli, 2025) , using the 'mlr3' ecosystem.