# POETREE: Interpretable Policy Learning with Adaptive Decision Trees

arXiv: https://arxiv.org/abs/2203.08057 · HTML: https://mlsanity.com/paper/2203.08057/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Distilling a Neural Network Into a Soft Decision Tree](https://mlsanity.com/paper/1711.09784/) (2017)
- [Programmatically Interpretable Reinforcement Learning](https://mlsanity.com/paper/1804.02477/) (2018)
- [Imitation Learning via Off-Policy Distribution Matching](https://mlsanity.com/paper/1912.05032/) (2019)
- [Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction](https://mlsanity.com/paper/1703.01030/) (2017)
- [Adaptive Neural Trees](https://mlsanity.com/paper/1807.06699/) (2018)
- [Scalable Bayesian Inverse Reinforcement Learning](https://mlsanity.com/paper/2102.06483/) (2021)
- [Human-in-the-Loop Interpretability Prior](https://mlsanity.com/paper/1805.11571/) (2018)
- [What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes](https://mlsanity.com/paper/2011.05064/) (2020)
- [CDT: Cascading Decision Trees for Explainable Reinforcement Learning](https://mlsanity.com/paper/2011.07553/) (2020)
- [Inverse Active Sensing: Modeling and Understanding Timely Decision-Making](https://mlsanity.com/paper/2006.14141/) (2020)
- [The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation](https://mlsanity.com/paper/2106.04240/) (2021)
- [Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies](https://mlsanity.com/paper/2203.07338/) (2022)

## Built on by (most cited first, 14 of 14)

- [Dense Reward for Free in Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2402.00782/) (2024)
- [MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees](https://mlsanity.com/paper/2209.07225/) (2022)
- [Tree Variational Autoencoders](https://mlsanity.com/paper/2306.08984/) (2023)
- [Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding](https://mlsanity.com/paper/2306.01157/) (2023)
- [Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning](https://mlsanity.com/paper/2310.07918/) (2023)
- [Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes](https://mlsanity.com/paper/2211.06138/) (2022)
- [Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning](https://mlsanity.com/paper/2210.05320/) (2022)
- [Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications](https://mlsanity.com/paper/2403.18316/) (2024)
- [Optimising Human-AI Collaboration by Learning Convincing Explanations](https://mlsanity.com/paper/2311.07426/) (2023)
- [Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models](https://mlsanity.com/paper/2503.16724/) (2025)
- [Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes](https://mlsanity.com/paper/2211.07052/) (2022)
- [How Should We Represent History in Interpretable Models of Clinical Policies?](https://mlsanity.com/paper/2412.07895/) (2024)
- [Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data](https://mlsanity.com/paper/2607.14318/) (2026)
- [MotherTree: Meta-learning on synthetic data improves decision tree training](https://mlsanity.com/paper/2610.10832/) (2026)

## Abstract (arXiv)

Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established policy learning approaches remain focused on imitation performance, they fall short of explaining the demonstrated decision-making process. Policy Extraction through decision Trees (POETREE) is a novel framework for interpretable policy learning, compatible with fully-offline and partially-observable clinical decision environments -- and builds probabilistic tree policies determining physician actions based on patients' observations and medical history. Fully-differentiable tree architectures are grown incrementally during optimization to adapt their complexity to the modelling task, and learn a representation of patient history through recurrence, resulting in decision tree policies that adapt over time with patient information. This policy learning method outperforms the state-of-the-art on real and synthetic medical datasets, both in terms of understanding, quantifying and evaluating observed behaviour as well as in accurately replicating it -- with potential to improve future decision support systems.