2025/04/06 by Daijiro Kabata, Kabata, Daijiro, Nicholas C. Henderson +3 · 1 citation
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2504.04571
openalex publication_date 2025/04/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Estimation of individualized treatment effects (ITE), also known as conditional average treatment effects (CATE), is an active area of methodology development. However, much less attention has been paid to the quantification of uncertainty of ITE/CATE estimates in right-censored survival data. Here we undertake an extensive simulation study to examine the coverage of interval estimates from two popular estimation algorithms, Bayesian additive regression trees (BART) and causal survival forest (CSF). We conducted simulation designs from 3 different settings: first, in a setting where BART was developed for an accelerated failure time model; second, where CSF was developed; and finally, a ``neutral'' simulation taken from a setting where neither BART nor CSF was developed. BART outperformed CSF in all three simulation settings. Both the BART and CSF algorithms involve multiple hyperparameters, and BART credible intervals had better coverage than the CSF confidence intervals under the default values, as well as under optimized values, of these hyperparameters.