From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies

• URL: http://arxiv.org/abs/2507.11381v2
• Date: Wed, 16 Jul 2025 10:38:29 GMT
• Title: From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
• Authors: Rom Gutman, Shimon Sheiba, Omer Noy Klein, Naama Dekel Bird, Amit Gruber, Doron Aronson, Oren Caspi, Uri Shalit,
• Abstract summary: We propose a framework for building patient-specific treatment recommendation models.<n>We focus on safety and validity, including the crucial issue of causal identification.
• Score: 7.619520924233835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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