迈向可靠的社交A/B测试：具有鲁棒后实验分析的溢出受限聚类
Towards Reliable Social A/B Testing: Spillover-Contained Clustering with Robust Post-Experiment Analysis
AI总结：
本文提出一种具有溢出受限的A/B测试框架，通过预实验阶段的平衡聚类算法和后实验阶段的定制估计器，有效减少溢出影响，提升统计推断的可靠性。
摘要：
A/B测试是在线平台决策的基础，然而社交产品常受到网络干扰：用户互动导致处理效应溢出到对照组。此类溢出会偏因果估计并削弱实验结论。现有方法面临关键限制：用户层面的随机化忽略了网络结构，而基于聚类的方法往往依赖通用聚类，未针对溢出控制进行定制，并在大规模情况下难以平衡无偏性和统计效力。我们提出一个具有溢出受限的实验框架，分为两个阶段。在预实验阶段，我们构建社交互动图并引入一种平衡的Louvain算法，该算法生成稳定、大小平衡的聚类同时最小化跨聚类边，从而实现可靠的基于聚类的随机化。在后实验阶段，我们开发了一种定制的CUPAC估计器，利用预实验行为协变量来减少由聚类层面分配引起的方差，从而提高统计效力。这些组件共同提供了结构化的溢出控制和稳健的统计推断。我们通过大规模的社交分享实验在快手平台（服务数以千万计的用户）上验证了我们的方法。结果表明，我们的方法显著减少了溢出，并比传统用户层面设计提供了更准确的社会策略评估，建立了可靠的、可扩展的网络A/B测试框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
