Debiased machine learning for counterfactual survival functionals based on left-truncated right-censored data
基于左截断右删失数据的反事实生存泛函去偏机器学习
Learning causal effects of a binary exposure on time-to-event endpoints can be challenging because survival times may be partially observed due to censoring and systematically biased due to truncation. In this work, we present debiased machine learning-based nonparametric estimators of the joint distribution of a counterfactual survival time and baseline covariates for use when the observed data are subject to covariate-dependent left truncation and right censoring and when…