Understanding causal relationships in dynamic systems is essential for numerous scientific fields, including epidemiology, economics, and biology. While causal inference methods have been extensively studied, they often rely on fully specified causal graphs, which may not always be available or practical in complex dynamic systems. Partially specified causal graphs, such as summary causal graphs (SCGs), provide a simplified representation of causal relationships, omitting temporal information and focusing on high-level causal structures. This simplification introduces new challenges concerning the types of queries of interest: macro queries, which involve relationships between clusters represented as vertices in the graph, and micro queries, which pertain to relationships between variables that are not directly visible through the vertices of the graph. In this paper, we first clearly distinguish between macro conditional independencies and micro conditional independencies and between macro total effects and micro total effects. Then, we demonstrate the soundness and completeness of the d-separation to identify macro conditional independencies in SCGs. Furthermore, we establish that the do-calculus is sound and complete for identifying macro total effects in SCGs. Conversely, we also show through various examples that these results do not hold when considering micro conditional independencies and micro total effects.

翻译：理解动态系统中的因果关系对于流行病学、经济学和生物学等众多科学领域至关重要。尽管因果推断方法已得到广泛研究，但它们通常依赖于完全指定的因果图，而在复杂的动态系统中，这种因果图可能并不总是可用或实用。部分指定的因果图，如摘要因果图（SCG），提供了因果关系的简化表示，省略了时间信息并专注于高层级因果结构。这种简化引入了关于感兴趣查询类型的新挑战：宏观查询涉及图中表示为顶点的聚类之间的关系，而微观查询则涉及图中顶点未直接显示的变量之间的关系。本文首先明确区分了宏观条件独立性与微观条件独立性，以及宏观总效应与微观总效应。然后，我们证明了d-分离在识别SCG中宏观条件独立性方面的可靠性与完备性。此外，我们确立了do-演算对于识别SCG中宏观总效应的可靠性与完备性。相反，我们也通过各种示例表明，当考虑微观条件独立性与微观总效应时，这些结论并不成立。