# Relative Sparsity for Medical Decision Problems

arXiv: https://arxiv.org/abs/2211.16566 · HTML: https://mlsanity.com/paper/2211.16566/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 83 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 24 of 24)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Best Subset Selection via a Modern Optimization Lens](https://mlsanity.com/paper/1507.03133/) (2015)
- [Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1705.08422/) (2017)
- [Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning](https://mlsanity.com/paper/1611.03531/) (2016)
- [Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions](https://mlsanity.com/paper/2002.03478/) (2020)
- [POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning](https://mlsanity.com/paper/2001.04032/) (2020)
- [Batch size-invariance for policy optimization](https://mlsanity.com/paper/2110.00641/) (2021)
- [Nonparametric causal effects based on incremental propensity score interventions](https://mlsanity.com/paper/1704.00211/) (2017)
- [Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient](https://mlsanity.com/paper/2011.04019/) (2020)
- [PEGASUS: A Policy Search Method for Large MDPs and POMDPs](https://mlsanity.com/paper/1301.3878/) (2013)
- [Cooperative learning for multiview analysis](https://mlsanity.com/paper/2112.12337/) (2021)
- [Regularization Matters in Policy Optimization](https://mlsanity.com/paper/1910.09191/) (2019)
- [Regularized Off-Policy TD-Learning](https://mlsanity.com/paper/2006.05314/) (2020)
- [Nonparametric inverse probability weighted estimators based on the highly adaptive lasso](https://mlsanity.com/paper/2005.11303/) (2020)
- [Robust Q-learning](https://mlsanity.com/paper/2003.12427/) (2020)
- [Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring](https://mlsanity.com/paper/2012.03294/) (2020)
- [Policy Optimization with Sparse Global Contrastive Explanations](https://mlsanity.com/paper/2207.06269/) (2022)
- [Valid post-selection inference in Robust Q-learning](https://mlsanity.com/paper/2208.03233/) (2022)

## Built on by (most cited first, 2 of 2)

- [Inference for relative sparsity](https://mlsanity.com/paper/2306.14297/) (2023)
- [Treatment, evidence, imitation, and chat](https://mlsanity.com/paper/2506.23040/) (2025)

## Abstract (arXiv)

Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (e.g., whether to administer hypotension treatment based on covariates blood pressure and heart rate). There is great interest in using such data-driven policies in healthcare. However, it is often important to explain to the healthcare provider, and to the patient, how a new policy differs from the current standard of care. This end is facilitated if one can pinpoint the aspects of the policy (i.e., the parameters for blood pressure and heart rate) that change when moving from the standard of care to the new, suggested policy. To this end, we adapt ideas from Trust Region Policy Optimization (TRPO). In our work, however, unlike in TRPO, the difference between the suggested policy and standard of care is required to be sparse, aiding with interpretability. This yields ``relative sparsity," where, as a function of a tuning parameter, λ, we can approximately control the number of parameters in our suggested policy that differ from their counterparts in the standard of care (e.g., heart rate only). We propose a criterion for selecting λ, perform simulations, and illustrate our method with a real, observational healthcare dataset, deriving a policy that is easy to explain in the context of the current standard of care. Our work promotes the adoption of data-driven decision aids, which have great potential to improve health outcomes.