A1596
Title: Who with whom: Learning optimal matching policies
Authors: Toru Kitagawa - Brown University (United States) [presenting]
Yagan Hazard - Collegio Carlo Alberto (Italy)
Abstract: Many economic contexts exist where the productivity and welfare performance of institutions and policies depend on who matches with whom. Examples include caseworkers and job seekers in job search assistance programs, medical doctors and patients, teachers and students, attorneys and defendants, and tax auditors and taxpayers, among others. Although reallocating individuals through a change in matching policy can be less costly than training personnel or introducing a new program, methods for learning optimal matching policies and their statistical performance are less studied than methods for other policy interventions. A method is developed to learn welfare optimal matching policies for two-sided matching problems in which a planner matches individuals based on a rich set of observable characteristics of the two sides. The learning problem is formulated as an empirical optimal transport problem with a match cost function estimated from training data, and an optimal matching policy is estimated by maximizing the entropy regularized empirical welfare criterion. A welfare regret bound for the estimated policy is derived and its convergence is characterized. The proposal is applied to the problem of matching caseworkers and job seekers in a job search assistance program, and its welfare performance is assessed in a simulation study calibrated with French administrative data.
