Coordinating Treatment Allocation and Recommendation

[2606.21120] Coordinating Treatment Allocation and Recommendation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Theoretical Economics arXiv:2606.21120 (econ) [Submitted on 19 Jun 2026 ( v1 ), last revised 15 Aug 2026 (this version, v4)] Title: Coordinating Treatment Allocation and Recommendation Authors: Li Guo , Penghuan Yan View a PDF of the paper titled Coordinating Treatment Allocation and Recommendation, by Li Guo and 1 other authors View PDF HTML (experimental) Abstract: We study a model in which a sender allocates limited treatment to agents with heterogeneous quality and later recommends selected agents to a receiver, seeking to maximize the number of agents accepted by the receiver. All agents value treatment, which improves agents' quality, but treatment must be allocated before the sender observes agents' initial quality; recommendation occurs only after quality is learned. A natural benchmark is to design the two instruments separately: allocate treatment randomly first, and then recommend agents from the top down afterward. Our main result shows that the sender can do strictly better by coordinating treatment allocation with recommendations. In the optimal joint mechanism, treatment is non-monotone in quality: an intermediate group has a lower treatment probability than both higher- and lower-quality agents, but is compensated with a guaranteed recommendation when treatment is realized. We provide an implementation through contracts that induce self-selection and discuss applications to education, industrial policy, and startup incubation. Comments: 35 pages including cover page Subjects: Theoretical Economics (econ.TH) Cite as: arXiv:2606.21120 [econ.TH] (or arXiv:2606.21120v4 [econ.TH] for this version) https://doi.org/10.48550/arXiv.2606.21120 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Penghuan Yan [ view email ] [v1] Fri, 19 Jun 2026 05:47:42 UTC (28 KB) [v2] Thu, 16 Jul 2026 08:15:13 UTC (28 KB) [v3] Thu, 13 Aug 2026 02:06:49 UTC (28 KB) [v4] Sat, 15 Aug 2026 04:04:47 UTC (28 KB) Full-text links: Access Paper: View a PDF of the paper titled Coordinating Treatment Allocation and Recommendation, by Li Guo and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: econ.TH < prev | next > new | recent | 2026-06 Change to browse by: econ References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Coordinating Treatment Allocation and Recommendation#608912
• sxy19095760/Health-Path-Recommendation-Algorithm: First public release#68596
• sxy19095760/Health-Path-Recommendation-Algorithm: First public release#68595
• 38 U.S.C. § 1173 — Formal evaluation of recommendations#587325
• Keeping the Index Open: The Recommendation-Side Cost of Shared Search and Recommendation#674576