A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing

• 成果类型: Article
• 署名作者: Yuan, Yuan; Altenburger, Kristen M.
• 署名单位: University of California System; University of California Davis
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2023.0462
• 发表日期: 2025
• 关键词: Causal Inference; marketplace; diversity; DESIGN; motifs

摘要

Problem definition: The reliability of controlled experiments, commonly referred to as A/B tests, is often compromised by network interference, where the outcomes of individual units are influenced by interactions with others. Significant challenges in this domain include the lack of accounting for complex social network structures and the difficulty in suitably characterizing network interference. Methodology/results: To address these challenges, we propose a machine learning-based method. We introduce causal network motifs and utilize transparent machine learning models to characterize network interference patterns underlying an A/B test on networks. Our method's performance has been demonstrated through simulations on both a synthetic experiment and a large-scale test on Instagram. Our experiments show that our approach outperforms conventional methods such as design-based cluster randomization and conventional analysis-based neighborhood exposure mapping. Managerial implications: Our approach provides a comprehensive and automated solution to address network interference for A/B testing practitioners. This aids in informing strategic business decisions in areas such as marketing effectiveness and product customization.