From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
This work addresses the challenge of deriving clinical recommendations from observational data, which is an incremental advancement in causal inference for healthcare.
The paper tackles the problem of generating patient-specific treatment recommendations from observational data, proposing a causal framework that integrates existing methods into a practical pipeline. In a real-world use-case for heart failure patients with acute kidney injury, the results suggest the pipeline can improve patient outcomes over current treatments.
We propose a framework for building patient-specific treatment recommendation models, building on the large recent literature on learning patient-level causal models and inspired by the target trial paradigm of Hernan and Robins. We focus on safety and validity, including the crucial issue of causal identification when using observational data. We do not provide a specific model, but rather a way to integrate existing methods and know-how into a practical pipeline. We further provide a real world use-case of treatment optimization for patients with heart failure who develop acute kidney injury during hospitalization. The results suggest our pipeline can improve patient outcomes over the current treatment regime.