Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding
This work addresses challenges in epidemiology and other fields where fully specified causal graphs are unavailable, providing theoretical foundations for causal inference in partially specified graphs, though it is incremental as it extends existing methods to a new graph type.
The paper tackled the problem of analyzing causal relations in complex dynamic systems using summary causal graphs (SCGs) with latent confounding, by distinguishing between macro and micro queries and proving the soundness and completeness of d-separation for macro conditional independencies and do-calculus for macro total effects in SCGs.
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.