In this paper, we examine the biases that arise when firms run A/B tests on continuous parameters to estimate global treatment effects on performance metrics of interest; we particularly focus on price experiments to measure the price impact on quantity demanded, and on profit. In canonical A/B experimental estimators, biases emerge due to interference between market participants. We employ structural modeling and differential calculus to derive intuitive characterizations of these biases. We then specialize our general model to the standard revenue-management pricing problem. This setting highlights a fundamental risk innate to A/B pricing experiments: that the canonical estimator for the expected change in profits, counterintuitively, can have the wrong sign in expectation. In other words, following the guidance of canonical estimators may lead firms to move prices (or fees) in the wrong direction, inadvertently decreasing profits. We introduce a novel debiasing technique for these canonical experiments, requiring only that firms equally split units between treatment and control. We apply these results to a two-sided market model, and demonstrate how the "change of sign" regime depends on market factors such as the supply/demand imbalance, and the price markup. We conclude by calibrating our revenue-management pricing model to published empirical estimates from Airbnb marketplaces, demonstrating that estimators with the wrong sign are not a knife-edge issue, and that they may be prevalent enough to be of concern to practitioners.

翻译：本文研究了企业在连续参数上进行A/B测试以估计全局处理效应时产生的偏差问题，特别聚焦于测量价格对需求量和利润影响的定价实验。在经典A/B实验估计量中，偏差源于市场参与者之间的相互干扰。我们采用结构建模和微分学方法，推导出这些偏差的直观特征表达式。随后将通用模型具体应用于标准收益管理定价问题。该场景揭示了A/B定价实验固有的根本风险：经典估计量对预期利润变化的估计值，反直觉地可能在期望意义上呈现错误符号。换言之，遵循经典估计量的指引可能导致企业向错误方向调整价格（或费用），从而意外降低利润。我们针对此类经典实验提出了一种新颖的去偏技术，仅要求企业将实验单元在实验组与对照组间平均分配。我们将这些结果应用于双边市场模型，论证了"符号反转"机制如何取决于供需失衡程度和价格加成等市场因素。最后通过将收益管理定价模型与已发布的Airbnb市场实证估计值进行校准，证明符号错误的估计量并非极端特例，其普遍性足以引起实践者的关注。