Lee Bounds with a Continuous Treatment in Sample Selection
Release time：12 May 2026
Apr
17
|
Time & Date
|
15:30 pm,
April
17,
2026
(Friday)
|
| Topic | Lee Bounds with a Continuous Treatment in Sample Selection |
| Time&Date |
02:00 pm-03:30 pm, April 17, 2026 (Friday) |
| Venue | Room 904, Teaching Complex D Building |
| Speaker |
Ying-Ying Lee University of California, Irvine |
| 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. |
| Biography | Ying-Ying Lee is an associate Professor at University of California, Irvine. She is currently an Associate Editor of the Journal of Econometrics. Her 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. She has published papers in the Review of Economics and Statistics, Journal of Econometrics, Journal of Business & Economic Statistics and Journal of American Statistical Association, among others. She earned a Ph.D. in Economics from University of Wisconsin-Madison, and was a postdoctoral research fellow at Nuffield College, University of Oxford. |
