This work develops a flexible inferential framework for nonparametric causal inference in time-to-event settings, based on stochastic interventions defined through multiplicative scaling of the intensity governing an intermediate event process. These interventions induce a family of estimands indexed by a scalar parameter α, representing effects of modifying event rates while preserving the temporal and covariate-dependent structure of the observed data generating mechanism. To enhance interpretability, we introduce calibrated interventions, where α is chosen to achieve a pre-specified goal, such as a desired level of cumulative risk of the intermediate event, and define corresponding composite target parameters capturing the downstream effects on the outcome process. This yields clinically meaningful contrasts while avoiding unrealistic deterministic intervention regimes. Under a nonparametric model, we derive efficient influence curves for α-indexed, calibrated, and composite target parameters and establish their double robustness properties. We further sketch a targeted maximum likelihood estimation (TMLE) strategy that accommodates flexible, machine learning based nuisance estimation. The proposed framework applies broadly to (causal) questions involving time-to-event treatments or mediators and is illustrated through different examples event-history settings. A simulation study demonstrates finite-sample inferential properties, and highlights the implications of practical positivity violations when interventions extend beyond observed data support.

翻译：本研究针对时间-事件场景中的非参数因果推断，建立了一个灵活的推断框架。该框架基于通过对中间事件过程强度进行乘法缩放定义的随机干预。这些干预产生了一族由标量参数α索引的估计量，代表了在保持观测数据生成机制的时间与协变量依赖结构的同时，修改事件发生率所产生的效应。为提高可解释性，我们引入了校准干预，即选择α以实现预先设定的目标（例如达到中间事件的期望累积风险水平），并定义了相应的复合目标参数以捕捉对结果过程的下游效应。这产生了具有临床意义的对比，同时避免了不切实际的确定性干预机制。在非参数模型下，我们推导了α索引参数、校准参数及复合目标参数的有效影响曲线，并建立了它们的双重稳健性。我们进一步概述了一种靶向最大似然估计策略，该策略支持基于机器学习的灵活干扰项估计。所提出的框架广泛适用于涉及时间-事件处理或中介变量的因果问题，并通过不同的事件历史场景示例进行了说明。一项模拟研究展示了有限样本下的推断性质，并强调了当干预超出观测数据支持范围时，实际正性条件违背所带来的影响。