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 baseline covariates suffice to deconfound the relationship between exposure and survival time. Our inferential procedures explicitly allow the integration of flexible machine learning tools for nuisance estimation, and enjoy certain robustness properties. The approach we propose can be directly used to make pointwise or uniform inference on smooth summaries of the joint counterfactual survival time and covariate distribution, and can be valuable even in the absence of interventions, when summaries of a marginal survival distribution are of interest. We showcase how our procedures can be used to learn a variety of inferential targets and illustrate their performance in simulation studies.

翻译：学习二元暴露对时间-事件终点的因果效应可能具有挑战性，因为生存时间可能因删失而部分观测到，并因截断而存在系统性偏差。在本工作中，我们提出了基于去偏机器学习的非参数估计量，用于估计反事实生存时间与基线协变量的联合分布，适用于观测数据受到协变量依赖的左截断和右删失影响，且基线协变量足以解混暴露与生存时间之间关系的情况。我们的推断程序明确允许集成灵活的机器学习工具进行干扰参数估计，并具备一定的稳健性特性。我们提出的方法可直接用于对反事实生存时间与协变量分布的联合分布的平滑摘要进行逐点或一致推断，即使在无干预的情况下，当关注边际生存分布的摘要时，该方法也具有重要价值。我们展示了如何使用我们的程序来学习多种推断目标，并通过模拟研究说明了其性能。