In this paper, we examine biases arising in A/B tests where firms modify a continuous parameter, such as price, to estimate the global treatment effect of a given performance metric, such as profit. These biases emerge in canonical experimental estimators due to interference among market participants. We employ structural modeling and differential calculus to derive intuitive characterizations of these biases. We then specialize our general model to a standard revenue management pricing problem. This setting highlights a key pitfall in the use of A/B pricing experiments to guide profit maximization: notably, the canonical estimator for the expected change in profits can have the {\em wrong sign}. In other words, following the guidance of canonical estimators may lead firms to move prices in the wrong direction, inadvertently decreasing profits relative to the status quo. We apply these results to a two-sided market model and show how this ``change of sign" regime depends on model parameters such as market imbalance, as well as the price markup. Finally, we discuss structural and practical implications for platform operators.

翻译：本文研究了在A/B测试中，当企业调整价格等连续参数以估计利润等给定绩效指标的整体处理效应时出现的偏差问题。这些偏差源于市场参与者之间的干扰，在经典实验估计量中表现显著。我们采用结构建模与微积分方法，推导出这些偏差的直观特征。随后将一般模型特化至标准收益管理定价问题中，这揭示了使用A/B定价实验指导利润最大化的关键陷阱：值得注意的是，利润预期变化的经典估计量可能出现“错误符号”。换言之，遵循经典估计量的指引可能导致企业朝着错误方向调整价格，无形中使利润低于现状水平。我们将这些结果应用于双边市场模型，并展示了这种“符号反转”机制如何取决于市场失衡度、价格加成等模型参数。最后，从结构层面与实践层面讨论了该现象对平台运营商的启示。