Double machine learning fixed effects. (2022) using the mlr3 ecosystem and the same notation.

Double machine learning fixed effects. Our new procedures are extensions of the well-known correlated random effects, within-group and first-difference estimators from linear to nonlinear panel models, specifically, Robinson (1988)’s partially linear regression model with fixed effects and unspecified nonlinear confounding. This approach combines traditional econometric techniques with modern machine learning tools to improve the accuracy and robustness of causal inferences. (2022) using the mlr3 ecosystem and the same notation. . 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. Dec 13, 2023 · Recent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. The xtdml package is built on the DoubleML package by Bach et al. Our new procedures are extensions of the well The xtdml package implements double machine learning (DML) for static partially linear regression models for panel data with fixed effects, as in Clarke and Polselli (2025). Apr 25, 2025 · Our new procedures are extensions of the well-known correlated random effects, within-group, and first-difference estimators from linear to non-linear panel models, specifically, the partially linear regression model with fixed effects and unspecified non-linear confounding. (2018) for the estimation of the homogeneous treatment effect in panel data models with unobserved individual heterogeneity (or fixed effects) and no unobserved confounding by extending Robinson (1988)’s partially linear regression model. Implementation of partially linear panel regression (PLPR) models with high-dimensional confounding variables and exogenous treatment variable within the double machine learning framework. 1093/ectj Feb 28, 2024 · We use Double Machine Learning (DML) by Chernozhukov et al. In this paper, we develop novel double machine learning (DML) procedures for panel data in which these algorithms are used to approximate high-dimensional and nonlinear nuisance functions of the covariates. Jan 2, 2025 · The paper introduces a "Double Machine Learning" (DML) framework for estimating causal effects in static panel data models with fixed effects. ytejfbj gat dzuc hfe disgp tuiy zixnel jerujeb wpgw jngf