This paper develops a practical framework for using observational data to audit the consumer surplus effects of AI-driven decisions, specifically in targeted pricing and algorithmic lending. Traditional approaches first estimate demand functions and then integrate to compute consumer surplus, but these methods can be challenging to implement in practice due to model misspecification in parametric demand forms and the large data requirements and slow convergence of flexible nonparametric or machine learning approaches. Instead, we exploit the randomness inherent in modern algorithmic pricing, arising from the need to balance exploration and exploitation, and introduce an estimator that avoids explicit estimation and numerical integration of the demand function. Each observed purchase outcome at a randomized price is an unbiased estimate of demand and by carefully reweighting purchase outcomes using novel cumulative propensity weights (CPW), we are able to reconstruct the integral. Building on this idea, we introduce a doubly robust variant named the augmented cumulative propensity weighting (ACPW) estimator that only requires one of either the demand model or the historical pricing policy distribution to be correctly specified. Furthermore, this approach facilitates the use of flexible machine learning methods for estimating consumer surplus, since it achieves fast convergence rates by incorporating an estimate of demand, even when the machine learning estimate has slower convergence rates. Neither of these estimators is a standard application of off-policy evaluation techniques as the target estimand, consumer surplus, is unobserved. To address fairness, we extend this framework to an inequality-aware surplus measure, allowing regulators and firms to quantify the profit-equity trade-off. Finally, we validate our methods through comprehensive numerical studies.

翻译：本文提出了一种利用观测数据评估人工智能驱动决策（特别是定向定价和算法贷款）对消费者剩余影响的实用框架。传统方法首先估计需求函数，然后通过积分计算消费者剩余，但这些方法在实践中往往难以实施：参数化需求形式存在模型设定偏误，而灵活的非参数或机器学习方法则需要大量数据且收敛速度缓慢。为此，我们利用现代算法定价中因平衡探索与利用而产生的固有随机性，提出了一种无需显式估计需求函数或进行数值积分的估计量。每个随机价格下的观测购买结果都是需求的无偏估计，通过采用新颖的累积倾向权重对购买结果进行精细重加权，我们能够重构积分过程。基于这一思想，我们进一步提出了增强型累积倾向加权估计量——一种双重稳健的变体，仅需需求模型或历史定价策略分布其中之一被正确设定即可。此外，该方法促进了灵活机器学习方法在消费者剩余估计中的应用：即使机器学习估计量收敛较慢，通过引入需求估计量仍可获得较快的收敛速率。由于目标估计量（消费者剩余）不可直接观测，上述估计量均非离策略评估技术的标准应用。针对公平性问题，我们将该框架扩展至具有不平等意识的剩余度量，使监管机构与企业能够量化利润与公平的权衡关系。最后，我们通过系统的数值研究验证了所提方法的有效性。