基于飞轮效应的分享诱导参与度的因果估计
Causal Estimation of Share-Induced Engagement with Flywheel Effects
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中文总结 AI 辅助
研究在线平台分享功能的飞轮效应，开发考虑干扰的实验框架，提出基于流量平衡恒等式的估计器，降低偏差，建立A/A测试程序，扩展到用户级指标，并用真实平台验证方法。
中文摘要 AI 辅助
在线平台的可持续用户增长不仅依赖获取新用户，还需通过社交分享功能重新激活和吸引现有用户。精心设计的分享功能可触发自我强化的“飞轮效应”。衡量此类分享功能的因果影响具有挑战性，因其效应通过复杂社交网络和时间级联展开，违反经典A/B测试的无干扰假设。我们开发了一个用于分享功能实验的框架，该框架考虑了飞轮效应引起的干扰，并针对分享诱导的参与度目标全局处理效应。我们的估计器由流量平衡恒等式驱动，将分享诱导的参与度解释为几何放大过程，产生一个封闭形式的传播调整，使用常用的归因日志来考虑多轮扩散。在温和条件下，我们建立了所提出估计器的一致性，并开发了用于管道验证的有效A/A测试程序。模拟研究表明，我们的方法相对于均值差估计器和一阶调整大幅降低了偏差，而所提出的A/A测试保持了名义I型错误。我们还通过泊松近似将框架扩展到用户级重新激活指标。最后，我们在一个真实的大规模在线平台上展示了该方法，并讨论了评估分享功能设计的实证意义。
英文摘要
Sustainable user growth in online platforms depends not only on acquiring new users but also on reactivating and engaging existing ones through social sharing features. A well-designed sharing feature can trigger a self-reinforcing ``flywheel effect'': reactivated users become potential sharers whose engagement propagates through the network over multiple rounds, amplifying total engagement. Measuring the causal impact of such sharing features is challenging, as their effects unfold through complex social networks and temporal cascades, violating the no-interference assumption underlying classical A/B testing. We develop a framework for experiments on sharing features that accounts for interference caused by the flywheel effect and targets a global treatment effect on share-induced engagement. Our estimator is motivated by a flow-balance identity and interprets share-induced engagement as a geometric amplification process, yielding a closed-form propagation adjustment that accounts for multi-round diffusion using commonly available attribution logs. Under mild conditions, we establish consistency of the proposed estimator and develop a valid A/A testing procedure for pipeline validation. Simulation studies show that our method substantially reduces bias relative to the difference-in-means estimator and first-order adjustments, while the proposed A/A test maintains nominal Type I error. We also extend the framework to a user-level reactivation metric via a Poisson approximation. Finally, we demonstrate the approach on a real-world large-scale online platform and discuss empirical implications for evaluating sharing feature designs.
