Falling Trees: A Model Class for Interpretable Risk Prioritization

2026-09-20

AI Summary

本文提出了一种新的可解释模型——falling trees，用于解决高风险案例优先级排序问题。通过引入树状分支结构并使用GRAVITree算法，提高了模型的表达能力。

Abstract

Many real-world decisions require prioritizing high-risk cases, such as clinicians prioritizing high-risk patients before lower-risk ones. Falling rule lists (FRLs), which are ordered if--then rules with monotonically decreasing risks, provide an interpretable framework for such tasks; however, their single-path structure yields a highly restricted model class. We introduce falling trees, a new family of interpretable models that enforces the same monotonic risk constraint while permitting tree-structured branching. We present GRAVITree, a novel dynamic-programming-with-bounds algorithm for learning the Rashomon set of falling trees under depth and branching constraints. Our formulation can interpolate between rule lists and full decision trees, enabling user-desired model expressivity. In a new clinical dataset and in many public classification benchmarks, falling trees match or outperform FRLs and other interpretable baselines, often producing more sparse decisions for high-risk instances. Our results show that falling trees strike a practical balance between interpretability, expressiveness, and risk prioritization for high-stakes settings.

Problem

• Falling Trees
• Risk Prioritization
• Interpretable Models

Innovation

• falling trees
• monotonic risk constraint
• tree-structured branching
• GRAVITree algorithm
• Rashomon set