Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of Criteria
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.
ProofPaper ↗
Key points
- 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.
- 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.
Sources (1)
- [1]Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of CriteriaarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 12:41 PM
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.
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.
Extractive summary: sentences quoted from the sources.