Ying-Ying Lee

Ying-Ying Lee is an Associate Professor of Economics at the University of California, Irvine, and an Associate Editor of the Journal of Econometrics. She holds a Ph.D. in Economics from the University of Wisconsin-Madison and completed a postdoctoral fellowship at Nuffield College, University of Oxford. Her research focuses on micro-econometrics with an emphasis on causal inference for continuous and multivalued treatments, leveraging nonparametric estimation and machine learning techniques.

Her work addresses challenges such as sample selection bias, monotonicity assumptions, and high-dimensional data in causal inference frameworks. Key contributions include advancements in double debiased machine learning for continuous treatment effects, regression discontinuity designs, and quantile derivative estimation. She has applied these methods to policy evaluation, workforce programs, and structural models.

• Education: PhD in Economics (University of Wisconsin-Madison), Postdoc (Nuffield College, Oxford)
• Roles: Associate Editor of Journal of Econometrics, Principal Investigator of multiple research projects
• Research Themes: Continuous treatment effects, causal bounds, instrumental variables, policy analysis

Her recent work emphasizes methodological innovations for handling complex treatment structures and selection mechanisms. Lee’s papers consistently bridge econometric theory with practical applications in labor economics and public policy. She actively contributes to open-source tools through GitHub repositories for her methodologies.