# Optimal Policy Trees

arXiv: https://arxiv.org/abs/2012.02279 · HTML: https://mlsanity.com/paper/2012.02279/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 19 of 19)

- [Constrained Prescriptive Trees via Column Generation](https://mlsanity.com/paper/2207.10163/) (2022)
- [Interpretable Personalized Experimentation](https://mlsanity.com/paper/2111.03267/) (2021)
- [A Machine Learning Approach to Two-Stage Adaptive Robust Optimization](https://mlsanity.com/paper/2307.12409/) (2023)
- [ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription](https://mlsanity.com/paper/2307.15691/) (2023)
- [Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making](https://mlsanity.com/paper/2309.00805/) (2023)
- [Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data](https://mlsanity.com/paper/2202.10944/) (2022)
- [Reducing Air Pollution through Machine Learning](https://mlsanity.com/paper/2303.12285/) (2023)
- [Learning Prescriptive ReLU Networks](https://mlsanity.com/paper/2306.00651/) (2023)
- [The R.O.A.D. to precision medicine](https://mlsanity.com/paper/2311.01681/) (2023)
- [Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning](https://mlsanity.com/paper/2405.20486/) (2024)
- [Adaptive Forests For Classification](https://mlsanity.com/paper/2510.22991/) (2025)
- [Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach](https://mlsanity.com/paper/2512.09198/) (2025)
- [Holistic Deep Learning](https://mlsanity.com/paper/2110.15829/) (2021)
- [Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation](https://mlsanity.com/paper/2212.06312/) (2022)
- [PresAIse, A Prescriptive AI Solution for Enterprises](https://mlsanity.com/paper/2402.02006/) (2024)
- [Applications of 0-1 Neural Networks in Prescription and Prediction](https://mlsanity.com/paper/2402.18851/) (2024)
- [Robust personalized pricing under uncertainty of purchase probabilities](https://mlsanity.com/paper/2407.15332/) (2024)
- [Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization](https://mlsanity.com/paper/2509.08194/) (2025)
- [An Interpretable AI Tool for SAVR vs TAVR in Low to Intermediate Risk Patients with Severe Aortic Stenosis](https://mlsanity.com/paper/2512.10308/) (2025)

## Abstract (arXiv)

We propose an approach for learning optimal tree-based prescription policies directly from data, combining methods for counterfactual estimation from the causal inference literature with recent advances in training globally-optimal decision trees. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems.