Expert-Guided g-computation with Large Language Models for Estimating Causal Effects on Timings: Applications to Hospital Quality Improvement

2026-08-10

AI Summary

This study addresses the challenge of accurately estimating the causal effects of multiple interventions on average length of stay in hospital quality improvement, where data scarcity and complex underlying mechanisms hinder reliable inference. The authors propose expert-guided g-computation (egg-computation), a novel framework that integrates Gantt charts with causal directed acyclic graphs (DAGs) to unify expert knowledge and empirical evidence. Clinical expert judgment is selectively incorporated only where causal identification is otherwise impossible, and large language models (LLMs) are leveraged to scalably generate causal graphs and estimates of time savings. In simulations, the method outperforms conventional causal inference approaches; when applied to evaluate eleven real-world hospital interventions, LLM-assisted results show high concordance with human expert assessments, demonstrating an efficient and scalable solution for causal effect estimation.

Abstract

Hospital quality improvement (QI) programs routinely face multiple candidate interventions to optimize hospital flow, but existing methods struggle to estimate and rank the causal effects of such interventions. This work focuses on one of the most standard hospital metrics, the average length of stay (LOS), and its causal estimand, the average time saved. To characterize this causal effect, qualitative approaches rely on expert judgment to map patient trajectories, making them susceptible to cognitive biases; quantitative approaches rely on data-driven models, which fail when interventions are hypothetical with no historical data or have complex causal mechanisms that require clinical reasoning rather than data alone. We propose expert-guided g-computation, or egg-computation, which combines the complementary strengths of both approaches by connecting the Gantt charts commonly used to map patient trajectories with the causal DAG literature. We introduce a causal model over Gantt charts and establish identification using a variant of g-computation that seeks expert input only for components unidentifiable from data. To make egg-computation practical, we develop an LLM-assisted pipeline that reliably scales up expert reasoning. In simulations, egg-computation outperforms conventional causal inference methods when patients have diverse causal structures and intervention mechanisms. In a study of eleven candidate QI interventions at an urban safety-net hospital, the LLM pipeline generated graphs and time-saving estimates highly concordant with those of human experts. Beyond healthcare, egg-computation is a broadly applicable framework for estimating the average time saved for candidate interventions whose causal mechanisms can be represented using Gantt charts.

Problem

• causal effects
• length of stay
• hospital quality improvement
• g-computation
• intervention evaluation

Innovation

• expert-guided g-computation
• causal inference
• Gantt charts
• large language models
• length of stay