From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909691254145024 |
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| author | Gutman, Rom Sheiba, Shimon Klein, Omer Noy Bird, Naama Dekel Gruber, Amit Aronson, Doron Caspi, Oren Shalit, Uri |
| author_facet | Gutman, Rom Sheiba, Shimon Klein, Omer Noy Bird, Naama Dekel Gruber, Amit Aronson, Doron Caspi, Oren Shalit, Uri |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies Gutman, Rom Sheiba, Shimon Klein, Omer Noy Bird, Naama Dekel Gruber, Amit Aronson, Doron Caspi, Oren Shalit, Uri Machine Learning Applications 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. |
| title | From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2507.11381 |