Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding
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含潜在混杂的汇总因果图中宏观条件独立与总效应的识别
- 研究问题
- 当动态系统只以省略时间细节、允许环的汇总因果图表示时，如何区分变量簇层面的宏观查询与单个变量层面的微观查询，并识别前者？
- 方法与思路
- 作者先区分宏观与微观条件独立及总效应，再研究 d 分离和 do 演算在汇总因果图中识别宏观查询的可靠性、完备性及不可识别情形。
- 材料陈述的贡献
- 摘要称，文章证明 d 分离对宏观条件独立识别的可靠性与完备性，以及 do 演算对宏观总效应识别的对应性质，并给出不可识别的图形刻画。
- 阅读关注点
- 应核对变量簇、干预和宏观总效应的定义，潜在混杂及环的表示规则，并查看可靠性、完备性证明所针对的图与查询范围。
- 阅读边界
- 所用摘要来自 arXiv 预印本，同行评审及期刊接收未核实；摘要未提供完整图模型条件，文中宏观指变量簇层级，不能直接理解为宏观经济。
原文摘要
Understanding causal relations in dynamic systems is essential in epidemiology. While causal inference methods have been extensively studied, they often rely on fully specified causal graphs, which may not always be available in complex dynamic systems. Partially specified causal graphs, and in particular summary causal graphs (SCGs), provide a simplified representation of causal relations between time series when working spacio-temporal data, omitting temporal information and focusing on causal structures between clusters of of temporal variables. Unlike fully specified causal graphs, SCGs can contain cycles, which complicate their analysis and interpretation. In addition, their cluster-based nature 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. Finally, we give a graphical characterization for the non-identifiability of macro total effects in SCGs.