# Optimal Sparse Survival Trees

arXiv: https://arxiv.org/abs/2401.15330 · HTML: https://mlsanity.com/paper/2401.15330/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Optimal Sparse Decision Trees](https://mlsanity.com/paper/1904.12847/) (2019)
- [Generalized and Scalable Optimal Sparse Decision Trees](https://mlsanity.com/paper/2006.08690/) (2020)
- [DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values](https://mlsanity.com/paper/1908.02337/) (2019)

## Built on by (most cited first, 5 of 5)

- [Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance](https://mlsanity.com/paper/2409.12788/) (2024)
- [Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect](https://mlsanity.com/paper/2510.12734/) (2025)
- [FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models](https://mlsanity.com/paper/2410.19081/) (2024)
- [Discovering Subgroups with Exceptional Survival Characteristics](https://mlsanity.com/paper/2602.22179/) (2026)
- [Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis](https://mlsanity.com/paper/2605.30119/) (2026)

## Abstract (arXiv)

Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health. Tree-based methods have been widely adopted for survival analysis due to their appealing interpretablility and their ability to capture complex relationships. However, most existing methods to produce survival trees rely on heuristic (or greedy) algorithms, which risk producing sub-optimal models. We present a dynamic-programming-with-bounds approach that finds provably-optimal sparse survival tree models, frequently in only a few seconds.