{"ID":2846559,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01329","arxiv_id":"2511.01329","title":"Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework","abstract":"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.","short_abstract":"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 selec...","url_abs":"https://arxiv.org/abs/2511.01329","url_pdf":"https://arxiv.org/pdf/2511.01329v1","authors":"[\"Ying Song\",\"Yijing Wang\",\"Hui Yang\",\"Weihan Jin\",\"Jun Xiong\",\"Congyi Zhou\",\"Jialin Zhu\",\"Xiang Gao\",\"Rong Chen\",\"HuaGuang Deng\",\"Ying Dai\",\"Fei Xiao\",\"Haihong Tang\",\"Bo Zheng\",\"KaiFu Zhang\"]","published":"2025-11-03T08:29:21Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}