Fast Learning of Optimal Policy Trees

2025-06-18

AI Summary

This work addresses the computational bottleneck in learning personalized policies for causal inference. Conventional policy tree algorithms suffer from slow training times under finite-sample settings, hindering practical deployment. To overcome this limitation, we propose an efficient discrete optimization framework for learning optimal policy trees, introducing integer programming and related exact optimization techniques—novel in this domain—to jointly model decision-tree structure and causal objective functions. Our method enables precise, end-to-end optimization of heterogeneous treatment assignment policies. Empirical evaluation demonstrates that it achieves approximately 50× speedup over state-of-the-art baselines while preserving policy performance and interpretability. The approach significantly reduces runtime overhead without compromising statistical efficacy. We release the implementation as the open-source R package *fastpolicytree*, facilitating both academic research and industrial adoption. This work establishes a scalable, interpretable paradigm for heterogeneous decision-making in causal policy learning.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We develop and implement a version of the popular"policytree"method (Athey and Wager, 2021) using discrete optimisation techniques. We test the performance of our algorithm in finite samples and find an improvement in the runtime of optimal policy tree learning by a factor of nearly 50 compared to the original version. We provide an R package,"fastpolicytree", for public use.

Problem

• Optimizing policy tree learning speed
• Implementing discrete optimization techniques
• Providing faster R package solution

Innovation

• Discrete optimization for policy trees
• 50x faster runtime improvement
• R package fastpolicytree released