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arXiv · 2511.01329

Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework

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
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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.

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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. 2025-11-03. Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework. https://arxiv.org/abs/2511.01329

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