# A Causal Framework for Evaluating ICU Discharge Strategies

arXiv: https://arxiv.org/abs/2603.25397 · HTML: https://mlsanity.com/paper/2603.25397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI](https://mlsanity.com/paper/2409.14160/) (2024)
- [G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes](https://mlsanity.com/paper/2406.05504/) (2024)
- [Optimal discharge of patients from intensive care via a data-driven policy learning framework](https://mlsanity.com/paper/2112.09315/) (2021)
- [Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models](https://mlsanity.com/paper/2110.03117/) (2021)
- [Using an Instrumental Variable to Test for Unmeasured Confounding](https://mlsanity.com/paper/1601.06288/) (2016)

## Abstract (arXiv)

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.