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.

翻译：我们开发了一种贝叶斯树集成模型，用于估计存在高维协变量的删失生存数据中的异质性处理效应。不同于通过树结构施加稀疏性，我们直接将马鞍形先验置于步长上，以实现自适应全局-局部收缩。该策略允许灵活的规则化并降低噪声。我们在树集成框架内开发了一种可逆跳跃吉布斯采样器，以适应非共轭的马鞍形先验。通过大量模拟实验表明，该方法能够准确估计高维协变量空间中不同稀疏程度及非线性处理效应函数下的处理效应。我们进一步通过对癌症基因组图谱中胰腺导管腺癌（PDAC）生存数据的再分析，展示了所提方法的实用价值。