Horseshoe Forests for High-Dimensional Causal Survival Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 115 references that are in the map. First posted on arXiv 29 Jul 2025.
Builds on
32 of 115 references.
References · 32
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- 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 255
- 2017Bayesian Compression for Deep Learningcited by 116
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- 2020Estimating heterogeneous treatment effects with right-censored data via causal survival forestscited by 29
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- 2022Bayesian Causal Inference: A Critical Reviewcited by 21
20 more
- 2020Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Reviewcited by 20
- 2017Lasso Meets Horseshoe : A Surveycited by 17
- 2018On Theory for BARTcited by 17
- 2017Individualized Treatment Effects with Censored Data via Fully Nonparametric Bayesian Accelerated Failure Time Modelscited by 13
- 2016Regularization and confounding in linear regression for treatment effect estimationcited by 12
- 2018Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Scorecited by 12
- 2020Estimating heterogeneous survival treatment effect in observational data using machine learningcited by 12
- 2016On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Priorcited by 10
- 2017The Graphical Horseshoe Estimator for Inverse Covariance Matricescited by 9
- 2022Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacycited by 8
- 2017Dynamic Shrinkage Processescited by 7
- 2018Variable Selection with ABC Bayesian Forestscited by 7
- 2021Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimationcited by 7
- 2021Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributionscited by 4
- 2016Doubly robust matching estimators for high dimensional confounding adjustmentcited by 3
- 2020A flexible parametric accelerated failure time modelcited by 3
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- 2019Averaging causal estimators in high dimensionscited by 2
- 2021Improved inference for doubly robust estimators of heterogeneous treatment effectscited by 1
- 2022A flexible approach for causal inference with multiple treatments and clustered survival outcomescited by 1
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