Who Should be with Whom? Learning Optimal Matching Policies
Release time:03 June 2025
Jun
04
Time & Date
|
10:30 am
-
12:00 pm,
June
04,
2025
(Wednesday)
|
Topic: | Who Should be with Whom? Learning Optimal Matching Policies |
Time&Date: | 10:30 am -12:00 pm, June 4, 2025 (Wednesday) |
Venue | Room 804, Teaching Complex D Building |
Speaker: |
Prof. Toru Kitagawa Brown University |
Abstract: | There are many contexts in economics where productivity and welfare performances of institutions and policies depend on who matches with whom. Examples include matching of caseworkers and job seekers in job search assistance programs, medical doctors and patients, teachers and students, attorneys and defendants, tax auditors and taxpayers, among others. Although reallocation of individuals through a change in matching policy can be less costly than directly training personnel or offering a new program, methods for learning optimal matching policies and their statistical performances are less studied. This paper develops a method to learn welfare optimal matching policies for two-sided matching problems in which a planner centrally prescribes who should match with whom based on the distributions of individual's observable characteristics of the two sides. We formulate the learning problem as an empirical optimal transport with the match cost function estimated from training data, and propose to estimate an optimal matching policy by optimizing the entropy regularized empirical welfare criterion. We derive a welfare regret bound of the estimated policy and characterize its convergence. We apply our proposal to the assignment problem of caseworkers to job seekers for a job search assistance program, and assess its welfare performance in a simulation study calibrated with French administrative data. |
Biography: | Toru Kitagawa is a Professor of Economics at Brown University. His research and teaching interests include econometrics, causal inference, statistical decision theory, Bayesian analysis, applied microeconomics, empirical macroeconomics, and experimental economics. His research projects center on how to use data to inform a better public policy. He studies theoretical econometrics and statistics to develop quantitative methods for evidence based policy decision, with applications to a wide range of applied economics including labor, development, and environmental economics. He also collaborates with empirical researchers in the world to conduct lab and field experiments to perform econometric analysis on human behaviors and learn better policies for real-world problems. He has published his works in major peer-reviewed journals in economics including Econometrica, Quarterly Journal of Economics, Journal of Econometrics, Quantitative Economics, and Journal of Business Economics and Statistics. He served as a board member of the Review of Economic Studies and an associate editor for Journal of Econometrics, and has been an associate editor of Journal of Business Economics and Statistics, Journal of Econometric Methods, and Japanese Economic Review. |