TY  - JOUR
AB  - This study examines the economic returns to education in the U.S. using 2024 CPS data and compares Ordinary Least Squares (OLS) regression with a Double Machine Learning (DML) framework incorporating models such as random forests, boosted trees, lasso, GAMs, and neural networks (MLP). Results show consistent returns of 8 to 9 percent per additional year of schooling across methods. Simulations reveal that all predictors perform well under linear assumptions if hyperparameters are optimally adjusted, while OLS/Lasso suffer from nonlinearity. Findings suggest that OLS remains robust in low-dimensional, near-linear contexts, offering practical guidance for economists and policymakers balancing model complexity and interpretability in education research.
AU  - Helal, Al Mansor 
AU  - Hiraki, Ryotaro 
AU  - Patrinos, Harry Anthony 
PY  - 2026
ST  - Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods
TI  - Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods
UR  - http://www.edworkingpapers.com/ai26-1473
ER  -