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

📝 Original Info

• Title: Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework
• ArXiv ID: 2511.01329
• Date: 2025-11-03
• Authors: ** 정보가 제공되지 않음 (논문에 저자 명시가 없으므로 “미제공”으로 표기) **

📝 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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