XplainAct让个体干预效果可视化，揭示不同人群的差异反应。
XplainAct: Visualization for Personalized Intervention Insights
- 基于因果推理构建个体级干预模拟与解释框架
- 在毒瘾致死和总统选举投票两案例中验证有效性
- 适合需要个性化决策支持的医疗与社会政策研究者
因果关系有助于人们理解复杂系统，尤其通过假设分析来探索干预措施如何改变结果。尽管现有方法已采用干预和反事实分析进行因果推理，但主要聚焦于群体层面的效果。这些方法在存在显著异质性的系统中表现不足，因为干预影响在不同子群体间差异巨大。为此，我们提出XplainAct，一个可视化分析框架，支持在子人群中对个体层面的干预进行模拟、解释与推理。我们在两个案例研究中展示了XplainAct的有效性：一是流行病学中的阿片类药物相关死亡分析，二是总统选举中投票倾向的研究。
原文摘要 · Abstract (English)
Causality helps people reason about and understand complex systems, particularly through what-if analyses that explore how interventions might alter outcomes. Although existing methods embrace causal reasoning using interventions and counterfactual analysis, they primarily focus on effects at the population level. These approaches often fall short in systems characterized by significant heterogeneity, where the impact of an intervention can vary widely across subgroups. To address this challenge, we present XplainAct, a visual analytics framework that supports simulating, explaining, and reasoning interventions at the individual level within subpopulations. We demonstrate the effectiveness of XplainAct through two case studies: investigating opioid-related deaths in epidemiology and analyzing voting inclinations in the presidential election.
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