Quantifying uncertainty of individualized treatment effects in right-censored survival data: A comparison of Bayesian additive regression trees and causal survival forest
右删失生存数据中个体化处理效应的不确定性:BART 与因果生存森林比较
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…