Causal knowledge graph analysis identifies adverse drug effects.

ABSTRACT:

Motivation

Knowledge graphs and structural causal models have each proven valuable for organizing biomedical knowledge and estimating causal effects, but remain largely disconnected: knowledge graphs encode qualitative relationships focusing on facts and deductive reasoning without formal probabilistic semantics, while causal models lack integration with background knowledge in knowledge graphs and have no access to the deductive reasoning capabilities that knowledge graphs provide.

Results

To bridge this gap, we introduce a novel formulation of Causal Knowledge Graphs (CKGs) which extend knowledge graphs with formal causal semantics, preserving their deductive capabilities while enabling principled causal inference. CKGs support deconfounding via explicitly marked causal ed

SUBMITTER: Toonsi S 

PROVIDER: S-EPMC12790815 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature