Hello and welcome! I am an associate professor of economics at University of California Irvine. I am currently an Associate Editor of the Journal of Econometrics.
My research interest is micro-econometrics and its applications, with a focus on causal inference of continuous and multivalued treatments using nonparametric estimation and machine learning. I earned my Ph.D. in Economics from University of Wisconsin-Madison, and was a postdoctoral research fellow at Nuffield College, University of Oxford.
I will be on sabbatical leave from August 2025 to July 2026 and visit the department of economics, the Chinese University of Hong Kong.
email: yingying.lee@uci.edu
Working papers:
Lee Bounds with a Continuous Treatment in Sample Selection (2025) with Chu-An Liu (draft) arXiv:2411.04312
Replication package of codes and data (link to download)
Abstract: We study causal inference in sample selection models where a continuous or multivalued treatment affects both outcomes and their observability (e.g., employment or survey response). We generalize the widely used Lee (2009)’s bounds for binary treatment effects. Our key innovation is a “sufficient treatment values” assumption that imposes weak restrictions on selection heterogeneity and is implicit in separable threshold-crossing models, including monotone effects on selection. Our double debiased machine learning estimator enables nonparametric and high-dimensional methods, using covariates to tighten the bounds and capture heterogeneity. Applications to Job Corps and CCC program evaluations reinforce prior findings under weaker assumptions.
Nonparametric Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments (Oct 2023), with Yingying Dong. arxiv.org2310.18504 (pdf)
Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models (2018). (arXiv)(SSRN) Working paper (2015) (pdf)
Published papers:
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments (2025), with Kyle Colangelo. (pdf) Accepted at Journal of Business & Economic Statistics
Code and dataset at Github. The first version was circulated as Lee (February 2019), “Double machine learning nonparametric inference on continuous treatment effects.” (December 2019 cemmap working paper CWP72/19) arXiv:2004.03036
Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-dimensional Data (2024), with Yu-Chin Hsu, Martin Huber, and Chu-An Liu, accepted at the Review of Economics and Statistics. (pdf)(Replication data)
Regression Discontinuity Designs with a Continuous Treatment, with Yingying Dong and Michael Guo, Journal of the American Statistical Association. (2023) 118:541, 208-221. (pdf) Supplemental Appendix (pdf)
Nonparametric Weighted Average Quantile Derivative, Econometric Theory (2022) 38, 497-535. (pdf)(SSRN)
Multivalued Treatments and Decomposition Analysis: An application to the WIA Program, with Wallice Ao and Sebastian Calonico, Journal of Business & Economic Statistics (2021) 39 (1), 358-371. (pdf)(SSRN)
Direct and Indirect Effects of Continuous Treatments Based on Generalized Propensity Score Weighting, with Yu-Chin Hsu, Martin Huber, and Layal Lettry, Journal of Applied Econometrics (2020) 35 (7), 814-840. (pdf) (R implementation: medweightcont) (data)
Applied Welfare Analysis for Discrete Choice with Interval-data on Income, with Debopam Bhattacharya, Journal of Econometrics (2019) 211 (2), 361-387. (pdf)(SSRN)
Partial Effects in Binary Response Models using a Special Regressor, with Hsueh-Hsiang Li, Economics Letters (2018) 169, 15-19. Supplemental Appendix. Stata code. (SSRN)
Efficient Propensity Score Regression Estimators of Multivalued Treatment Effects for the Treated, Journal of Econometrics (2018) 204 (2), 207-222. (SSRN)
Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification, Econometrics (2016) 4(1) 2. (pdf)