In epidemiology, 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 and observational data. It specifically focuses on identifying total causal changes and total effects in a nonparametric framework, as well as direct causal changes and direct effects in a linear context. In doing so, it provides a novel approach to causal reasoning that holds potential for various public health applications.

翻译：在流行病学中，理解不同人群间的因果机制对于设计有效的公共卫生干预措施至关重要。近年来，差异图作为一种可视化工具被提出，用于表示两个不同人群间的因果变异。尽管通过因果发现方法从数据中推断这些图已取得进展，但在系统性地利用其潜力以增强因果推理方面仍存在空白。本文通过建立利用差异图和观测数据识别因果变化与因果效应的条件，填补了这一空白。研究特别关注非参数框架下总因果变化与总效应的识别，以及线性情境下直接因果变化与直接效应的识别。通过这项工作，本文提出了一种具有多种公共卫生应用潜力的因果推理新方法。