Research
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
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 this setting and organize them into a hierarchy that clarifies their relationships and differences. In particular, we establish that a causal effect may be identified over a collection of graphs without admitting a single causal derivation that holds uniformly across all compatible graphs. These results help reveal gaps and clarify ambiguities in the existing literature.
Related
- Partial Identification under Causal Orders by Linear Programming
- Foundation Models for Partial Causal Identification
- Symmetries and Causality: Causal Effect Identification Beyond IID Data
- Robust Weighted Triangulation of Causal Effects Under Model Uncertainty
Source: arXiv cs.AI | 2026-10-12