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Proxy-Aided Demand Learning with an Application to Various Pricing Problems
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DOI：10.1287/opre.2025.1793.png)
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Abstract
En 中文
In data-driven demand learning, understanding customer willingness to pay presents a significant challenge because of the complex interplay between various influencing factors. This paper addresses the multifaceted relationship between key demand drivers (e.g., price) and demand outcomes (e.g., sales) and underscores the difficulties in identifying causal effects under endogeneity. To mitigate confounding bias, we introduce proxy variables into the demand learning process. Inspired by the proximal causal inference framework, we categorize proxies into outcome and treatment types, enabling the identification and estimation of demand outcomes, particularly expected potential sales at specific price points, through the use of a bridge function. The paper further explores practical applications of the proposed demand learning process in data-driven pricing problems, focusing typically on challenges in static and contextual pricing and then extending to broader decisionmaking problems. Thereafter, the regret bounds of these applications are also established. In addition, simulations and real data analysis demonstrate that our proposed method effectively addresses demand learning challenges and outperforms existing methodologies.
Keywords:
causal inference
data-driven decision making
demand learning
pricing
regret analysis