Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
右删失或左截断生存数据中异质处理效应曲线的目标学习
In recent years, there has been growing interest in causal machine learning estimators for quantifying subject-specific effects of a binary treatment on time-to-event outcomes. Estimation approaches have been proposed which attenuate the inherent regularisation bias in machine learning predictions, with each of these estimators addressing measured confounding, right censoring, and in some cases, left truncation. However, the existing approaches are found to exhibit…