Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of Criteria

arXiv:2610.13520v1 Announce Type: new Abstract: Identifying causal effects from observational data typically requires a fully specified causal graph, an assumption that is often unrealistic in complex or high-dimensional settings. Causal abstractions address this limitation by retaining only partial causal information, thereby defining equivalence classes of compatible causal graphs. In this paper, we study causal effect identifiability directly at the level of these graph collections, rather than at the level of individual causal diagrams. We formalize several notions of identifiability in th

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