Job Market Paper
Optimal treatment assignment rules under capacity constraints (with Kohei Izumi), available at arXiv:2506.12225.
Abstract: We study treatment assignment problems under capacity constraints, where a planner aims to maximize social welfare by assigning treatments based on observable covariates. Such constraints are common in practice, as treatments are often costly or limited in supply. However, they introduce nontrivial challenges for deriving optimal statistical assignment rules because the planner needs to coordinate treatment assignment probabilities across the entire covariate distribution. To address these challenges, we reformulate the planner's constrained maximization problem as an optimal transport problem, which makes the problem effectively unconstrained. We then establish locally asymptotic optimality results of assignment rules using a limits of experiments framework. Finally, we illustrate our method with a voucher assignment problem for private secondary school attendance using data from Angrist, Bettinger, and Kremer (2006).
Research
- Closed-form estimation of additively separable triangular equation models. (to appear soon)
- Applications of cross-fit variance estimator for model specification, testing overidentification, and structural parameter hypotheses (with Yukitoshi Matsushita and Taisuke Otsu), available at [.pdf].
- On large market asymptotics for spatial price competition models (with Taisuke Otsu), Economics Letters (2024), 234, 111468. doi: 10.1016/j.econlet.2023.111468. [.pdf] [Appendix]
- A characterization of the Esteban–Ray polarization measures (with Yoko Kawada and Yuta Nakamura), Economics Letters (2018), 169, 35–37. doi: 10.1016/j.econlet.2018.05.011.