# Safe and Interpretable Estimation of Optimal Treatment Regimes

arXiv: https://arxiv.org/abs/2310.15333 · HTML: https://mlsanity.com/paper/2310.15333/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 95 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Critic Regularized Regression](https://mlsanity.com/paper/2006.15134/) (2020)
- [d3rlpy: An Offline Deep Reinforcement Learning Library](https://mlsanity.com/paper/2111.03788/) (2021)
- [SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning](https://mlsanity.com/paper/1811.00090/) (2018)
- [Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho](https://mlsanity.com/paper/2108.02836/) (2021)
- [Beyond Reward: Offline Preference-guided Policy Optimization](https://mlsanity.com/paper/2305.16217/) (2023)
- [Scaling active inference](https://mlsanity.com/paper/1911.10601/) (2019)
- [Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions](https://mlsanity.com/paper/2303.17396/) (2023)
- [Interpretable Dynamic Treatment Regimes](https://mlsanity.com/paper/1606.01472/) (2016)
- [Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning](https://mlsanity.com/paper/2210.02552/) (2022)
- [Causal inference with misspecified exposure mappings: separating definitions and assumptions](https://mlsanity.com/paper/2103.06471/) (2021)
- [MALTS: Matching After Learning to Stretch](https://mlsanity.com/paper/1811.07415/) (2018)
- [Challenges for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2103.05612/) (2021)
- [Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine](https://mlsanity.com/paper/2204.07124/) (2022)
- [Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients](https://mlsanity.com/paper/2203.04920/) (2022)
- [Causal nearest neighbor rules for optimal treatment regimes](https://mlsanity.com/paper/1711.08451/) (2017)

## Built on by (most cited first, 1 of 1)

- [Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variation](https://mlsanity.com/paper/2606.13821/) (2026)

## Abstract (arXiv)

Recent statistical and reinforcement learning methods have significantly advanced patient care strategies. However, these approaches face substantial challenges in high-stakes contexts, including missing data, inherent stochasticity, and the critical requirements for interpretability and patient safety. Our work operationalizes a safe and interpretable framework to identify optimal treatment regimes. This approach involves matching patients with similar medical and pharmacological characteristics, allowing us to construct an optimal policy via interpolation. We perform a comprehensive simulation study to demonstrate the framework's ability to identify optimal policies even in complex settings. Ultimately, we operationalize our approach to study regimes for treating seizures in critically ill patients. Our findings strongly support personalized treatment strategies based on a patient's medical history and pharmacological features. Notably, we identify that reducing medication doses for patients with mild and brief seizure episodes while adopting aggressive treatment for patients in intensive care unit experiencing intense seizures leads to more favorable outcomes.