# Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes

arXiv: https://arxiv.org/abs/1504.07715 · HTML: https://mlsanity.com/paper/1504.07715/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2015.

## Built on by (most cited first, 11 of 11)

- [A Review of Off-Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2212.06355/) (2022)
- [Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data](https://mlsanity.com/paper/2301.05491/) (2023)
- [A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets](https://mlsanity.com/paper/2202.10574/) (2022)
- [Interpretable Off-Policy Learning via Hyperbox Search](https://mlsanity.com/paper/2203.02473/) (2022)
- [Stage-Aware Learning for Dynamic Treatments](https://mlsanity.com/paper/2310.19300/) (2023)
- [Doubly Robust Fusion of Many Treatments for Policy Learning](https://mlsanity.com/paper/2505.08092/) (2025)
- [GEAR: On Optimal Decision Making with Auxiliary Data](https://mlsanity.com/paper/2104.10573/) (2021)
- [Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design](https://mlsanity.com/paper/2210.06825/) (2022)
- [De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithm](https://mlsanity.com/paper/2603.02174/) (2026)
- [Meta-analysis of individualized treatment rules via sign-coherency](https://mlsanity.com/paper/2211.15476/) (2022)
- [A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning](https://mlsanity.com/paper/2310.09545/) (2023)

## Abstract (arXiv)

A treatment regime formalizes personalized medicine as a function from individual patient characteristics to a recommended treatment. A high-quality treatment regime can improve patient outcomes while reducing cost, resource consumption, and treatment burden. Thus, there is tremendous interest in estimating treatment regimes from observational and randomized studies. However, the development of treatment regimes for application in clinical practice requires the long-term, joint effort of statisticians and clinical scientists. In this collaborative process, the statistician must integrate clinical science into the statistical models underlying a treatment regime and the clinician must scrutinize the estimated treatment regime for scientific validity. To facilitate meaningful information exchange, it is important that estimated treatment regimes be interpretable in a subject-matter context. We propose a simple, yet flexible class of treatment regimes whose members are representable as a short list of if-then statements. Regimes in this class are immediately interpretable and are therefore an appealing choice for broad application in practice. We derive a robust estimator of the optimal regime within this class and demonstrate its finite sample performance using simulation experiments. The proposed method is illustrated with data from two clinical trials.