@TechReport{iza:izadps:dp18523,
            	author={Helal, Al Mansor and Hiraki, Ryotaro and Patrinos, Harry Anthony},
            	title={Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods},
            	year={2026},
	month={Apr},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={18523},
            	url={https://www.iza.org/publications/dp18523},
	abstract={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.},
	keywords={returns to education;machine learning},
            }