用差异图分析不同群体间的因果关系变化，助力公共卫生决策。
Causal reasoning in difference graphs
- 基于差异图构建非参数与线性设定下的因果推断框架
- 可识别总体因果变化与直接因果效应，支持跨群体比较
- 适用于政策评估、流行病学研究等需要跨人群分析的场景
理解不同人群间的因果机制对制定有效的公共卫生干预措施至关重要。最近提出的差异图可用于直观呈现两个不同群体之间的因果差异。尽管已有研究通过因果发现方法从数据中推断差异图，但如何系统性地利用其潜力以增强因果推理仍存在空白。本文填补了这一空白，建立了利用差异图识别因果变化与因果效应的条件。具体而言，论文在非参数设定下识别总体因果变化与总体效应，在线性设定下识别直接因果变化与直接效应。该研究提出了一种新颖的因果推理方法，具有广泛应用于公共卫生领域的潜力。
原文摘要 · Abstract (English)
Understanding causal mechanisms across different populations is essential for designing effective public health interventions. Recently, difference graphs have been introduced as a tool to visually represent causal variations between two distinct populations. While there has been progress in inferring these graphs from data through causal discovery methods, there remains a gap in systematically leveraging their potential to enhance causal reasoning. This paper addresses that gap by establishing conditions for identifying causal changes and effects using difference graphs. It specifically focuses on identifying total causal changes and total effects in a nonparametric setting, as well as direct causal changes and direct effects in a linear setting. In doing so, it provides a novel approach to causal reasoning that holds potential for various public health applications.
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