Proxy-Aided Demand Learning with an Application to Various Pricing Problems

Demand in the Shadows: Proxy-Based Solutions for Smarter Pricing 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 “Proxy-Aided Demand Learning with an Application to Various Pricing Problems,” Shen and Cui tackle this challenge by leveraging ideas from proximal causal inference. They introduce a framework that incorporates proxy variables—categorized into treatment and outcome types—to 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.