LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies
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. Using the MIMIC-IV database with 21,859 sepsis patients, we compare seven covariate-integration strategies for estimating the effect of early vasopressor initiation on 28-day mortality, spanning tabular-only baselines, traditional NLP repres...
| Authors | Lei Liu, Jialin Chen, Kathy Macropol |
|---|---|
| Primary category | cs.LG |
| Published | 18 April 2026 |
| Last updated at source | 23 July 2026 |
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