Reducing Marketplace Interference Bias Via Shadow Prices
利用影子价格降低交易平台的市场干扰偏差AI 解读 ↓
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利用影子价格降低交易平台的市场干扰偏差
- 研究问题
- 在线性规划匹配供需的交易平台中，用户之间的市场干扰使常规 A/B 指标有偏时，如何估计全平台实施处理的价值变化？
- 方法与思路
- 作者提出通过优化估计全处理与全对照价值，并以两组平均影子价格替代实际累计价值比较，分析流体极限和有限规模近似。
- 材料陈述的贡献
- 摘要称，优化估计在流体极限无偏，影子价格比较对应价值函数的一阶近似，并在一定假设下比常规随机试验估计更少偏。
- 阅读关注点
- 应核对平台匹配模型与干扰机制、全处理价值目标、流体极限定义，以及一阶近似误差和较低偏差结论所需的具体假设。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及接收状态未核实；流体极限无偏不等于有限系统无偏，较低偏差也不自动意味着更低均方误差。
原文摘要
Marketplace companies rely heavily on experimentation when making changes to the design or operation of their platforms. The workhorse of experimentation is the randomized controlled trial (RCT), or A/B test, in which users are randomly assigned to treatment or control groups. However, marketplace interference causes the Stable Unit Treatment Value Assumption (SUTVA) to be violated, leading to bias in the standard RCT metric. In this work, we propose techniques for platforms to run standard RCTs and still obtain meaningful estimates despite the presence of marketplace interference. We specifically consider a generalized matching setting, in which the platform explicitly matches supply with demand via a linear programming algorithm. Our first proposal is for the platform to estimate the value of global treatment and global control via optimization. We prove that this approach is unbiased in the fluid limit. Our second proposal is to compare the average shadow price of the treatment and control groups rather than the total value accrued by each group. We prove that this technique corresponds to the correct first-order approximation (in a Taylor series sense) of the value function of interest even in a finite-size system. We then use this result to prove that, under reasonable assumptions, our estimator is less biased than the RCT estimator. At the heart of our result is the idea that it is relatively easy to model interference in matching-driven marketplaces since, in such markets, the platform mediates the spillover.