报告题目：Who Should Be With Whom? Learning Optimal Matching Policies
主讲嘉宾：Kitagawa 教授现为布朗大学经济系讲席教授，主要研究领域包括因果推断、统计决策、贝叶斯方法、模型平均以及宏观计量经济学等。他的研究成果发表于多个国际顶级学术期刊，如 Quarterly Journal of Economics、Econometrica、Review of Economics and Statistics、Journal of Econometrics、Quantitative Economics、Journal of Business and Economic Statistics 以及 Journal of Economic Theory 等。Kitagawa 教授现任 Journal of Applied Econometrics、Econometric Reviews 与 Journal of Business and Economic Statistics 的副主编，并曾担任 The Review of Economic Studies 和 Journal of Econometrics 的副主编。
报告摘要：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 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.
报告时间：2025年6月10日(周二)，16:30-18:00
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