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Identifying Macro Causal Effects in C-DMGs over DMGs
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Shows that sigma-separation and the do-calculus are sound and complete for identifying macro causal effects in cluster-directed mixed graphs (C-DMGs) over cyclic DMGs induced by ioSCMs, and extends SC-hedge non-identifiability to such graphs when cyclic clusters have size at least 2.
Problem
Identifying macro-level causal effects between clusters of variables when the underlying causal structure may contain cycles (ioSCMs/DMGs) and only a partially specified cluster graph (C-DMG) is known.
Approach
Extends sigma-separation and the sigma-do-calculus to C-DMGs over DMGs, proving unconditional soundness and completeness of macro-level identification (unlike the ADMG case, which needs cluster-size conditions), and generalizes SC-hedge non-identifiability under a cluster-size-at-least-2 assumption for cyclic clusters.
Key results
- Proves sigma-separation is sound and complete in C-DMGs over DMGs (Theorems on soundness/completeness of sigma-separation).
- Proves do-calculus with sigma-separation is sound and complete for macro causal effects in C-DMGs over DMGs without cluster-size constraints.
- Shows SC-hedge existence implies non-identifiability in C-DMGs over DMGs when every cyclic cluster has size at least 2.
Surprising findings
- Unlike the ADMG setting, do-calculus completeness for macro effects holds unconditionally in C-DMGs over DMGs.
Limitations
- Completeness of the do-calculus is relative to compatible DMGs and does not cover other possible identification rules; sigma-do-calculus completeness in DMGs is not proven.
- The SC-hedge criterion is only proven sound under the assumption that clusters in cycles have size at least 2 and is not proven complete.
- No empirical evaluation; results are purely theoretical.
Experimental setup
Baselines: C-DMG over ADMGs identification results hedge criterion
Builds on: do-calculus sigma-separation ioSCM C-DMG over ADMGs SC-hedge
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