用因果模型区分社交影响中的相关与因果，更准预测信息传播效果。
Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement
- 结合时序模型与因果推断，同步建模干预时机与互动效应
- 在真实谣言数据上，预测准确率比基线高15%至22%
- 适合研究虚假信息传播、用户影响力评估的学者与从业者
理解社交媒体中真实影响力需区分相关性与因果性，尤其在分析虚假信息传播时。现有方法多关注曝光指标与网络结构，却难以捕捉外部时间信号引发互动的因果机制。本文提出一种联合处理-结果框架，利用现有时序模型同时适应政策实施时机与互动效果。将医疗领域因果推断技术应用于社交互动的时序特性，解决外部混杂信号带来的挑战。在真实世界谣言与误导信息数据集上的实验表明，本模型在多种反事实场景（包括曝光调整、时机变化、干预时长差异）下，对互动行为的预测性能优于现有基准15%至22%。对492名用户的案例研究显示，所提出的因果效应度量与专家基于经验的影响力标准高度一致。
原文摘要 · Abstract (English)
Understanding true influence in social media requires distinguishing correlation from causation--particularly when analyzing misinformation spread. While existing approaches focus on exposure metrics and network structures, they often fail to capture the causal mechanisms by which external temporal signals trigger engagement. We introduce a novel joint treatment-outcome framework that leverages existing sequential models to simultaneously adapt to both policy timing and engagement effects. Our approach adapts causal inference techniques from healthcare to estimate Average Treatment Effects (ATE) within the sequential nature of social media interactions, tackling challenges from external confounding signals. Through our experiments on real-world misinformation and disinformation datasets, we show that our models outperform existing benchmarks by 15--22% in predicting engagement across diverse counterfactual scenarios, including exposure adjustment, timing shifts, and varied intervention durations. Case studies on 492 social media users show our causal effect measure aligns strongly with the gold standard in influence estimation, the expert-based empirical influence.
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