Online marketplaces frequently run pricing experiments in environments where users choose from a list of items. In these settings, items compete for users' limited attention and demand, creating interference among items within a list: Changing prices for any item can affect the demand for others, biasing estimates from item-level A/B tests. Besides, a key consideration in pricing experiments is preserving platform coherency across prices and item availability. This requirement rules out experimental designs such as user-level A/B tests as they violate platform coherency. We propose Two-Sided Prioritized Ranking (TSPR) to estimate the total average treatment effect of price changes in such settings. TSPR exploits position bias in ranked search results to create variation in treatment exposure without compromising coherency. TSPR randomizes both users and items and reorders ranked lists, prioritizing treated items for one group of users and untreated items for the other. All users see the same items at consistent prices, but differ in exposure to treatment as they pay disproportionate attention across ranks. In semi-synthetic simulations based on Expedia hotel search data, TSPR outperforms baseline coherency-preserving experiment designs by reducing estimation bias and providing sufficient statistical power.

翻译：在线市场常在用户从项目列表中选择的环境中进行定价实验。在此类场景中，项目会争夺用户有限的注意力和需求，导致列表内项目间产生干扰：更改任一项目的价格都可能影响其他项目的需求，从而使项目级A/B测试的估计结果产生偏差。此外，定价实验的一个关键考量是保持价格与项目可用性之间的平台一致性。这一要求排除了用户级A/B测试等实验设计，因为它们会破坏平台一致性。本文提出双端优先排序方法，用于在此类场景中估计价格变化的总平均处理效应。该方法利用排序搜索结果中的位置偏差，在不破坏一致性的前提下创造处理暴露的变异。TSPR同时对用户和项目进行随机化处理，并重排有序列表——对一组用户优先展示处理过的项目，对另一组用户则优先展示未处理项目。所有用户均以一致价格看到相同的项目，但由于他们在不同排序位置上的注意力分配不均，其处理暴露程度存在差异。基于Expedia酒店搜索数据的半合成仿真表明，TSPR通过降低估计偏差并提供足够的统计功效，优于其他保持一致性的基线实验设计。