A Causal Framework for Evaluating ICU Discharge Strategies
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908915645546496 |
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| author | Simha, Sagar Nagaraj Ortholand, Juliette Dongelmans, Dave Workum, Jessica D. Thijssens, Olivier W. M. Abu-Hanna, Ameen Cinà, Giovanni |
| author_facet | Simha, Sagar Nagaraj Ortholand, Juliette Dongelmans, Dave Workum, Jessica D. Thijssens, Olivier W. M. Abu-Hanna, Ameen Cinà, Giovanni |
| contents | In this applied paper, we address the difficult open problem of when to discharge patients from the Intensive Care Unit. This can be conceived as an optimal stopping scenario with three added challenges: 1) the evaluation of a stopping strategy from observational data is itself a complex causal inference problem, 2) the composite objective is to minimize the length of intervention and maximize the outcome, but the two cannot be collapsed to a single dimension, and 3) the recording of variables stops when the intervention is discontinued. Our contributions are two-fold. First, we generalize the implementation of the g-formula Python package, providing a framework to evaluate stopping strategies for problems with the aforementioned structure, including positivity and coverage checks. Second, with a fully open-source pipeline, we apply this approach to MIMIC-IV, a public ICU dataset, demonstrating the potential for strategies that improve upon current care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25397 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Causal Framework for Evaluating ICU Discharge Strategies Simha, Sagar Nagaraj Ortholand, Juliette Dongelmans, Dave Workum, Jessica D. Thijssens, Olivier W. M. Abu-Hanna, Ameen Cinà, Giovanni Methodology Artificial Intelligence Machine Learning In this applied paper, we address the difficult open problem of when to discharge patients from the Intensive Care Unit. This can be conceived as an optimal stopping scenario with three added challenges: 1) the evaluation of a stopping strategy from observational data is itself a complex causal inference problem, 2) the composite objective is to minimize the length of intervention and maximize the outcome, but the two cannot be collapsed to a single dimension, and 3) the recording of variables stops when the intervention is discontinued. Our contributions are two-fold. First, we generalize the implementation of the g-formula Python package, providing a framework to evaluate stopping strategies for problems with the aforementioned structure, including positivity and coverage checks. Second, with a fully open-source pipeline, we apply this approach to MIMIC-IV, a public ICU dataset, demonstrating the potential for strategies that improve upon current care. |
| title | A Causal Framework for Evaluating ICU Discharge Strategies |
| topic | Methodology Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.25397 |