解决电商平台价格实验中的干扰问题，保持平台一致性的同时精准估计价格影响。
Two-Sided Prioritized Ranking: A Coherency-Preserving Design for Marketplace Experiments
- 通过双向优先排序，同时随机化用户和商品位置来创造处理差异。
- 在Expedia数据上，偏差降低且统计功效优于现有设计。
- 适合关注价格实验公平性与结果准确性的平台算法团队。
在线市场频繁在用户从列表中选择商品的环境中进行价格实验。由于商品间竞争有限注意力与需求，任一商品价格变动会影响其他商品需求，导致单品级A/B测试估计产生偏差。此外，定价实验需保持平台整体一致性，这排除了用户级实验设计。本文提出双侧优先排序（TSPR）方法，在不破坏一致性的前提下估计价格变动的总平均处理效应。TSPR利用搜索结果的位置偏倚，通过随机化用户与商品并重新排序列表，使一组用户更易看到被处理商品，另一组则更易看到未处理商品。所有用户看到相同商品与价格，但因关注度随排名变化而产生不同的处理暴露。基于Expedia酒店搜索数据的半合成仿真显示，TSPR相比基线一致性保留设计，显著降低估计偏差并具备足够统计功效。
原文摘要 · Abstract (English)
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.
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