这项工作考虑的是在具有右截尾和竞争风险特征的经典时间事件设置中，针对绝对风险和存活概率的治疗效应进行目标极大似然估计（TMLE）。 TMLE是一种通用方法，它在两步程序中结合了灵活的合奏学习和半参数效率理论，用于替代因果参数的估计。 我们将连续时间的TMLE方法专门用于竞争风险设置，并提出一种定向算法，通过迭代更新特定原因风险来解决目标参数的有效影响曲线方程。 作为工作的一部分，我们进一步详细说明和实现了最近提出的高度自适应套索估计器，该估计器与-惩罚泊松回归一起用于连续时间条件危害。 由此得到的估计程序仅依赖于对统计模型的非常温和的非参数限制，从而为连续时间事件数据的基于机器学习的半参数因果推断提供了新的工具。 我们将这些方法应用于一个公开可用的滤泡细胞淋巴瘤数据集中，该数据集中的受试者随着时间的推移进行关注，直到疾病复发或死亡而未复发。 数据显示出重要的时变效应，可以通过高度自适应套索来捕捉。 在我们的模拟中，我们比较了我们的方法与基于随机生存森林和离散时间TMLE的类似方法。 本文受版权保护。 保留所有权利。

This work considers targeted maximum likelihood estimation (TMLE) of treatment effects on absolute risk and survival probabilities in classical time-to-event settings characterized by right-censoring and competing risks. TMLE is a general methodology combining flexible ensemble learning and semiparametric efficiency theory in a two-step procedure for substitution estimation of causal parameters. We specialize and extend the continuous-time TMLE methods for competing risks settings, proposing a targeting algorithm that iteratively updates cause-specific hazards to solve the efficient influence curve equation for the target parameter. As part of the work, we further detail and implement the recently proposed highly adaptive lasso estimator for continuous-time conditional hazards with -penalized Poisson regression. The resulting estimation procedure benefits from relying solely on very mild nonparametric restrictions on the statistical model, thus providing a novel tool for machine learning based semiparametric causal inference for continuous-time time-to-event data. We apply the methods to a publicly available dataset on follicular cell lymphoma where subjects are followed over time until disease relapse or death without relapse. The data display important time-varying effects which can be captured by the highly adaptive lasso. In our simulations, that are designed to imitate the data, we compare our methods to a similar approach based on random survival forests and to the discrete-time TMLE. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.