AI RESEARCH
LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies
arXiv CS.LG
•
ArXi:2604.16763v1 Announce Type: new Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goals of care, and mental status are documented in free-text notes but absent from structured data. Large language models can extract these latent confounders as interpretable, structured covariates, yet how to effectively integrate them into causal estimation pipelines has not been systematically studied.