Causal Estimation of Share-Induced Engagement with Flywheel Effects
具有飞轮效应的分享诱导参与度因果估计AI 解读 ↓
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具有飞轮效应的分享诱导参与度因果估计
- 研究问题
- 当分享功能通过多轮社交传播重新激活用户并放大参与度时，如何在存在网络干扰的实验中估计其全局处理效应？
- 方法与思路
- 作者依据流量平衡恒等式将传播表示为几何放大，利用归因日志构造闭式调整，并提出一致性分析、A/A 验证与用户级近似扩展。
- 材料陈述的贡献
- 摘要称，该方法在模拟中比均值差和一阶调整偏差更小，A/A 检验维持名义错误率，并在大型线上平台展示实际应用。
- 阅读关注点
- 应核对流量平衡与几何传播的假设、归因日志覆盖、网络稳定性及一致性条件，并检查泊松近似和 A/A 验证的适用范围。
- 阅读边界
- 这是预印本，不能据此认定已同行评审或期刊接收。摘要未具体说明所谓温和条件，单一平台应用不足以保证任意传播网络有效。
原文摘要
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.