Horseshoe Forests for High-Dimensional Causal Survival Analysis
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915988176371712 |
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| author | Jacobs, Tijn van Wieringen, Wessel N. van der Pas, Stéphanie L. |
| author_facet | Jacobs, Tijn van Wieringen, Wessel N. van der Pas, Stéphanie L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Horseshoe Forests for High-Dimensional Causal Survival Analysis Jacobs, Tijn van Wieringen, Wessel N. van der Pas, Stéphanie L. Methodology Machine Learning 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. |
| title | Horseshoe Forests for High-Dimensional Causal Survival Analysis |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2507.22004 |