
Peering Through the Fog: A Hierarchy of Causal Identifiability Without Full Graphs
“In the absence of perfect knowledge, how do we still reason causally?” This paper tackles a profound and practical dilemma in causal inference: what if we don’t know the full causal graph? In real-world settings — whether in healthcare, finance, or digital platforms — complete causal diagrams are rare. Practitioners instead rely on causal abstractions: simplified, coarse-grained representations that preserve partial causal knowledge. But this raises a fundamental question: Which causal queries can still be identified under such abstraction? ...