Hello and welcome! I am an associate professor of economics at the department of Economics, 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.
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: Sample selection bias arises in causal inference when a treatment affects both the outcome and the researcher’s ability to observe it. This paper generalizes the sharp bounds in Lee (2009) for the average treatment effect of a binary treatment to a continuous/multivalued treatment. We revisit the Imbens, Rubin, and Sacerdote (2001) lottery data to study the effect of the prize on earnings that are only observed for the employed and the survey respondents. We evaluate the Job Crops program to study the effect of training hours on wages. To identify the average treatment effect of always-takers who are selected into samples with observed outcomes regardless of the treatment value they receive, we assume that if a subject is selected at some sufficient treatment values, then it remains selected at all treatment values. For example, if program participants are employed with one week of training, then they remain employed with any training hours. This sufficient treatment values assumption includes the monotone assumption on the treatment effect on selec- tion as a special case. We further allow the conditional independence assumption and subjects with different pretreatment covariates to have different sufficient treatment values. The practical estimation and inference theory utilize the orthogonal moment function and cross-fitting for double debiased machine learning.
Nonparametric Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments (Oct 2023), with Yingying Dong. arxiv.org2310.18504 (pdf)
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments (Sep 2023), with Kyle Colangelo. arXiv:2004.03036 (pdf).
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)
Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models (2018). (arXiv)(SSRN) Working paper (2015) (pdf)
Published papers:
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)