Towards Reliable Social A/B Testing: Spillover-Contained Clustering with Robust Post-Experiment Analysis

• URL: http://arxiv.org/abs/2602.08569v1
• Date: Mon, 09 Feb 2026 12:08:29 GMT
• Title: Towards Reliable Social A/B Testing: Spillover-Contained Clustering with Robust Post-Experiment Analysis
• Authors: Xu Min, Zhaoxu Yang, Kaixuan Tan, Juan Yan, Xunbin Xiong, Zihao Zhu, Kaiyu Zhu, Fenglin Cui, Yang Yang, Sihua Yang, Jianhui Bu,
• Abstract summary: A/B testing is the foundation of decision-making in online platforms, but social products often suffer from network interference.<n>We propose a spillover-contained experimentation framework with two stages.<n>We validate our approach through large-scale social sharing experiments on Kuaishou, a platform serving hundreds of millions of users.
• Score: 11.30339991179317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A/B testing is the foundation of decision-making in online platforms, yet social products often suffer from network interference: user interactions cause treatment effects to spill over into the control group. Such spillovers bias causal estimates and undermine experimental conclusions. Existing approaches face key limitations: user-level randomization ignores network structure, while cluster-based methods often rely on general-purpose clustering that is not tailored for spillover containment and has difficulty balancing unbiasedness and statistical power at scale. We propose a spillover-contained experimentation framework with two stages. In the pre-experiment stage, we build social interaction graphs and introduce a Balanced Louvain algorithm that produces stable, size-balanced clusters while minimizing cross-cluster edges, enabling reliable cluster-based randomization. In the post-experiment stage, we develop a tailored CUPAC estimator that leverages pre-experiment behavioral covariates to reduce the variance induced by cluster-level assignment, thereby improving statistical power. Together, these components provide both structural spillover containment and robust statistical inference. We validate our approach through large-scale social sharing experiments on Kuaishou, a platform serving hundreds of millions of users. Results show that our method substantially reduces spillover and yields more accurate assessments of social strategies than traditional user-level designs, establishing a reliable and scalable framework for networked A/B testing.

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