Horseshoe Forests for High-Dimensional Causal Survival Analysis

• URL: http://arxiv.org/abs/2507.22004v2
• Date: Wed, 30 Jul 2025 11:55:45 GMT
• Title: Horseshoe Forests for High-Dimensional Causal Survival Analysis
• Authors: Tijn Jacobs, Wessel N. van Wieringen, Stéphanie L. van der Pas,
• Abstract summary: We develop a Bayesian tree ensemble model to estimate heterogeneous treatment effects in censored survival data.<n>Instead of imposing sparsity through the tree structure, we place a horseshoe prior directly on the step heights to achieve adaptive global-local shrinkage.
• Score: 0.3441021278275805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop a Bayesian tree ensemble model to estimate heterogeneous treatment effects in censored survival data with high-dimensional covariates. Instead of imposing sparsity through the tree structure, we place a horseshoe prior directly on the step heights to achieve adaptive global-local shrinkage. This strategy allows flexible regularisation and reduces noise. We develop a reversible jump Gibbs sampler to accommodate the non-conjugate horseshoe prior within the tree ensemble framework. We show through extensive simulations that the method accurately estimates treatment effects in high-dimensional covariate spaces, at various sparsity levels, and under non-linear treatment effect functions. We further illustrate the practical utility of the proposed approach by a re-analysis of pancreatic ductal adenocarcinoma (PDAC) survival data from The Cancer Genome Atlas.

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