Trust the Edge, Doubt the Arrow: Auditing Validation Signals for Text-Mined Clinical Causal Knowledge Graphs

Su Lan, Shicheng Zhou, Yiqi Wang, Yanming Zhu, Xuefei Yin +1

Clinical causal retrieval-augmented generation systems increasingly use knowledge graphs (KGs) mined from notes, yet extracted assertions remain candidates that require audit for patient-level EHR support and direction. We construct a directed candidate graph from 328,839 MIMIC-IV discharge summaries using LLM extraction and UMLS normalization, and evaluate three signals against SemMedDB: within-admission co-occurrence lift, cross-admission diagnosis-coding temporal precedence, and literature corroboration. Lift filters unsupported co-mentions and provides useful EHR support, but not causal proof. Coding order is a weak direction signal: among the 686 candidates where it commits, it reaches 54.2% agreement with SemMedDB, below the extractor's 69.4% on the same subset. Literature corroboration supplies independent direction evidence but covers only 30.4% of candidate relations. A MIMIC-III replication yields the same pattern: extractor direction remains stable (70.5%), whereas coding-order direction is near chance (50.9%). Thus, text-mined clinical causal candidate KGs are best treated as EHR-supported but direction-uncertain. In a leakage-controlled causal QA benchmark, lift-only…