{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T01:12:23Z","timestamp":1783041143359,"version":"3.54.6"},"reference-count":82,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Operations Research"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>Demand in the Shadows: Proxy-Based Solutions for Smarter Pricing<\/jats:p>\n                  <jats:p>Understanding how price affects customer demand is a cornerstone of data-driven pricing, but traditional approaches often struggle under endogeneity because of the presence of confounding factors. In the paper \u201cProxy-Aided Demand Learning with an Application to Various Pricing Problems,\u201d Shen and Cui tackle this challenge by leveraging ideas from proximal causal inference. They introduce a framework that incorporates proxy variables\u2014categorized into treatment and outcome types\u2014to enable reliable identification and estimation of customer demand. Central to their method is the use of a bridge function that allows accurate recovery of potential sales at different price points. Besides theoretical and managerial insights, the paper demonstrates practical applications in both static and contextual pricing, with the proposed algorithms applied to a real-world e-commerce data set. Tellingly, the proposed framework offers a promising new direction for practitioners aiming to optimize pricing with confounded data.<\/jats:p>","DOI":"10.1287\/opre.2025.1793","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T15:27:21Z","timestamp":1763047641000},"page":"770-787","source":"Crossref","is-referenced-by-count":2,"title":["Proxy-Aided Demand Learning with an Application to Various Pricing Problems"],"prefix":"10.1287","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5464-4464","authenticated-orcid":false,"given":"Tao","family":"Shen","sequence":"first","affiliation":[{"name":"School of Management & Center for Data Science, Zhejiang University, Hangzhou, Zhejiang 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9957-7955","authenticated-orcid":false,"given":"Yifan","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Management & Center for Data Science, Zhejiang University, Hangzhou, Zhejiang 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"109","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1090.0725"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2020.3680"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2021.0781"},{"issue":"5","key":"B4","first-page":"2095","volume":"65","author":"Bernstein F","year":"2018","journal-title":"Management Sci."},{"key":"B5","volume-title":"Constrained Optimization and Lagrange Multiplier Methods","author":"Bertsekas DP","year":"2014"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1287\/ijoo.2022.0077"},{"key":"B7","unstructured":"Bertsimas D, Vayanos P (2017) Data-driven learning in dynamic pricing using adaptive optimization. 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