提出简单模型估算社交网络中分享行为的因果效应。
A Simple Model to Estimate Sharing Effects in Social Networks
- 基于马尔可夫决策过程建模用户分享行为
- 推导出无偏估计器，显著优于现有方法
- 适合研究社交网络干预效果的算法工程师
随机对照试验（RCT）是科学领域估计处理效应的黄金标准。科技公司采用A/B测试作为现代替代方案，将用户随机分配至不同系统版本并持续追踪行为，以评估处理对业务指标的因果影响。然而，当随机化单元（如用户）的结果不独立时，会干扰处理效应的识别，削弱决策者对系统的可观测性。社交网络正是此类情况的典型：其设计促进用户间互动，导致分享等行为产生自然干扰，严重阻碍效应测量。本文提出一种基于马尔可夫决策过程（MDP）的简化模型，描述社交网络中的用户分享行为。在该模型下，我们推导出处理效应的无偏估计器，并通过可复现的合成实验验证，其性能显著优于现有方法。
原文摘要 · Abstract (English)
Randomised Controlled Trials (RCTs) are the gold standard for estimating treatment effects across many fields of science. Technology companies have adopted A/B-testing methods as a modern RCT counterpart, where end-users are randomly assigned various system variants and user behaviour is tracked continuously. The objective is then to estimate the causal effect that the treatment variant would have on certain metrics of interest to the business. When the outcomes for randomisation units -- end-users in this case -- are not statistically independent, this obfuscates identifiability of treatment effects, and harms decision-makers' observability of the system. Social networks exemplify this, as they are designed to promote inter-user interactions. This interference by design notoriously complicates measurement of, e.g., the effects of sharing. In this work, we propose a simple Markov Decision Process (MDP)-based model describing user sharing behaviour in social networks. We derive an unbiased estimator for treatment effects under this model, and demonstrate through reproducible synthetic experiments that it outperforms existing methods by a significant margin.
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