Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
Clément Yvernes, Emilie Devijver, Adèle H. Ribeiro, Marianne Clausel--Lesourd, Éric Gaussier
TL;DR
The paper extends Cluster-DAGs to allow cycles at the cluster level by relaxing partition admissibility, enabling causal reasoning across arbitrary variable clusterings. It introduces a structure-of-interest framework, along with canonical and unfolded graphs, to derive a sound and atomically complete calculus for cluster-level interventions, closely aligning with Pearl's do-calculus. A key practical insight is that any cluster can be reduced to size at most 3 without changing the calculus outcomes, making computation tractable even for large clusters. This work broadens the applicability of C-DAGs to intractable real-world settings while preserving a principled identification theory and offering strategies to manage unknown cluster sizes. Future directions include achieving global completeness and extending the approach to micro-level interventions when only the C-DAG is known.
Abstract
Cluster DAGs (C-DAGs) provide an abstraction of causal graphs in which nodes represent clusters of variables, and edges encode both cluster-level causal relationships and dependencies arisen from unobserved confounding. C-DAGs define an equivalence class of acyclic causal graphs that agree on cluster-level relationships, enabling causal reasoning at a higher level of abstraction. However, when the chosen clustering induces cycles in the resulting C-DAG, the partition is deemed inadmissible under conventional C-DAG semantics. In this work, we extend the C-DAG framework to support arbitrary variable clusterings by relaxing the partition admissibility constraint, thereby allowing cyclic C-DAG representations. We extend the notions of d-separation and causal calculus to this setting, significantly broadening the scope of causal reasoning across clusters and enabling the application of C-DAGs in previously intractable scenarios. Our calculus is both sound and atomically complete with respect to the do-calculus: all valid interventional queries at the cluster level can be derived using our rules, each corresponding to a primitive do-calculus step.
