用实验内数据构建双边图，精准评估买卖双方的因果影响。
Towards Measuring Sell Side Outcomes in Buy Side Marketplace Experiments using In-Experiment Bipartite Graph
- 基于实验内交互数据构建双边图，避免依赖历史信息
- 在超过8000万用户的欧洲二手平台验证方法有效性
- 适合研究平台经济中跨方因果效应的学者与工程师
本研究在真实在线市场环境中评估了用于受控双边图实验的因果推断估计器。其创新之处在于利用实验内的数据构建双边图，而非依赖以往知识或历史数据——这在现有文献中是主流做法。我们通过买家与卖家在市场中的多种交互行为构建双边图，开创了双边实验与中介分析交叉的新方向。该方法对现代市场平台评估买方实验中的卖方因果效应（或反之）具有重要意义。研究基于Vinted平台的历史买方实验进行验证，该平台是欧洲最大的二手商品交易平台，用户数超过8000万。
原文摘要 · Abstract (English)
In this study, we evaluate causal inference estimators for online controlled bipartite graph experiments in a real marketplace setting. Our novel contribution is constructing a bipartite graph using in-experiment data, rather than relying on prior knowledge or historical data, the common approach in the literature published to date. We build the bipartite graph from various interactions between buyers and sellers in the marketplace, establishing a novel research direction at the intersection of bipartite experiments and mediation analysis. This approach is crucial for modern marketplaces aiming to evaluate seller-side causal effects in buyer-side experiments, or vice versa. We demonstrate our method using historical buyer-side experiments conducted at Vinted, the largest second-hand marketplace in Europe with over 80M users.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。