Interference Among First-Price Pacing Equilibria: A Bias and Variance Analysis
第一价格节奏均衡间的干扰：偏差与方差分析AI 解读 ↓
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第一价格节奏均衡间的干扰：偏差与方差分析
- 研究问题
- 在线市场买方预算使实验两组相互影响，而预算拆分过细又降低性能时，如何并行开展实验并校正市场间干扰偏差？
- 方法与思路
- 作者通过市场分割实施并行预算控制实验，用第一价格节奏均衡描述干扰，并借助数学规划敏感性分析构造去偏替代量。
- 材料陈述的贡献
- 摘要称替代量消除均衡的一阶偏差，给出代入估计器及渐近正态性，并在半合成数据例子中展示所需覆盖性质。
- 阅读关注点
- 应核对市场分割规则、替代量与原实验效应的差异、一阶去偏后剩余偏差，以及均衡敏感性和渐近推断的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审和期刊接收未经核实。摘要未给出分割条件及数值细节；消除一阶偏差不等于消除全部干扰。
原文摘要
Online A/B testing is widely used in the internet industry to inform decisions on new feature roll-outs. For online marketplaces (such as advertising markets), standard approaches to A/B testing may lead to biased results when buyers operate under a budget constraint, as budget consumption in one arm of the experiment impacts performance of the other arm. To counteract this interference, one can use a budget-split design where the budget constraint operates on a per-arm basis and each arm receives an equal fraction of the budget, leading to ``budget-controlled A/B testing.'' Despite clear advantages of budget-controlled A/B testing, performance degrades when budget are split too small, limiting the overall throughput of such systems. In this paper, we propose a parallel budget-controlled A/B testing design where we use market segmentation to identify submarkets in the larger market, and we run parallel experiments on each submarket. Our contributions are as follows: First, we introduce and demonstrate the effectiveness of the parallel budget-controlled A/B test design with submarkets in a large online marketplace environment. Second, we formally define market interference in first-price auction markets using the first price pacing equilibrium (FPPE) framework. Third, we propose a debiased surrogate that eliminates the first-order bias of FPPE, drawing upon the principles of sensitivity analysis in mathematical programs. Fourth, we derive a plug-in estimator for the surrogate and establish its asymptotic normality. Fifth, we provide an estimation procedure for submarket parallel budget-controlled A/B tests. Finally, we present numerical examples on semi-synthetic data, confirming that the debiasing technique achieves the desired coverage properties.