提出新方法，让搜索平台评估更准确地排除干扰影响。
Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework
- 用竞争隔离+倾向得分匹配，解决平台级评估中的干扰问题。
- 实验显示干扰效应和估计方差显著降低，结果更稳定。
- 适合做搜索系统平台评估的研究者与工程师参考。
在基于搜索的双边市场平台中，评估平台级干预措施面临系统性干扰，如溢出效应和网络干扰。尽管倾向得分匹配（PSM）与双重差分（DID）框架广泛用于因果推断，但仍易受选择偏差和未建模溢出带来的跨单元干扰影响。本文提出一种新型因果框架——竞争隔离PSM-DID，将倾向得分匹配与竞争隔离相结合，实现对平台级指标（如订单量、GMV）而非商品级指标的精准测量。该方法在互斥条件下可保证无偏估计，并开源了数据集以支持市场干扰的可复现研究（github.com/xxxx）。大量实验表明，相比基线方法，该框架显著降低了干扰效应与估计方差；在大规模平台上的成功部署验证了其实际应用价值。
原文摘要 · Abstract (English)
Evaluating platform-level interventions in search-based two-sided marketplaces is fundamentally challenged by systemic effects such as spillovers and network interference. While widely used for causal inference, the PSM (Propensity Score Matching) - DID (Difference-in-Differences) framework remains susceptible to selection bias and cross-unit interference from unaccounted spillovers. In this paper, we introduced Competitive Isolation PSM-DID, a novel causal framework that integrates propensity score matching with competitive isolation to enable platform-level effect measurement (e.g., order volume, GMV) instead of item-level metrics in search systems. Our approach provides theoretically guaranteed unbiased estimation under mutual exclusion conditions, with an open dataset released to support reproducible research on marketplace interference (github.com/xxxx). Extensive experiments demonstrate significant reductions in interference effects and estimation variance compared to baseline methods. Successful deployment in a large-scale marketplace confirms the framework's practical utility for platform-level causal inference.
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