arXiv 17 Jul 2025 · Econometrics
arXiv:2507.13567 · PDF · DOI · OpenAlex · Extracted main text
There are many economic contexts 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. This paper develops a method to learn welfare optimal matching policies for two-sided matching problems in which a planner matches individuals based on the rich set of observable characteristics of the two sides. We formulate the learning problem as an empirical optimal transport problem with a match cost function estimated from training data, and propose estimating an optimal matching policy by maximizing the entropy regularized empirical welfare criterion. We derive a welfare regret bound for the estimated policy and characterize its convergence. We apply our proposal to the problem of matching caseworkers and job seekers in a job search assistance program, and assess its welfare performance in a simulation study calibrated with French administrative data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dromundo, S. and A. Haramboure (2022) Do Job Counselors Matter? Measuring Counselor Value-Added in Job Search | 0.874 | 9 | 2 | 100% |
| 2 | Galichon, A. and B. Salanié (2022) Cupid's Invisible Hand: Social Surplus and Identification in Matching Models | 0.737 | 3 | 2 | 100% |
| 3 | Rigollet, P. and A. J. Stromme (2025) On the sample complexity of entropic optimal transport | 0.737 | 3 | 2 | 100% |
| 4 | Graham, B. S., G. W. Imbens, and G. Ridder (2014) Complementarity and aggregate implications of assortative matching: A nonparametric analysis | 0.737 | 3 | 2 | 100% |
| 5 | Cuturi, M (2013) Sinkhorn Distances: Lightspeed Computation of Optimal Transport, in | 0.644 | 2 | 2 | 100% |
| 6 | Behaghel, L., B. Crépon, and M. Gurgand (2014) Private and public provision of counseling to job seekers: Evidence from a large controlled experiment | 0.644 | 2 | 2 | 100% |
| 7 | Graham, B. S (2011) Econometric methods for the analysis of assignment problems in the presence of complementarity and social spillovers | 0.644 | 2 | 2 | 100% |
| 8 | Peyre, G. and M. Cuturi (2019) Computational Optimal Transport | 0.511 | 2 | 1 | 100% |
| 9 | Ai, C., Y. Fang, and H. Xie (2024) Data-driven policy learning for continuous treatments | 0.405 | 1 | 1 | 100% |
| 10 | Athey, S. and S. Wager (2021) Policy Learning With Observational Data | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 55 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference in partially identified moment models via regularized optimal transport | 0.644 | 2 | 2 |
| 2 | Optimal treatment assignment rules under capacity constraints | 0.405 | 1 | 1 |