We consider continuous-time survival or more general event-history settings, where the aim is to infer the causal effect of a time-dependent treatment process. This is formalised as the effect on the outcome event of a (possibly hypothetical) intervention on the intensity of the treatment process, i.e. a stochastic intervention. To establish whether valid inference about the interventional situation can be drawn from typical observational, i.e. non-experimental, data we propose graphical rules indicating whether the observed information is sufficient to identify the desired causal effect by suitable re-weighting. In analogy to the well-known causal directed acyclic graphs, the corresponding dynamic graphs combine causal semantics with local independence models for multivariate counting processes. Importantly, we highlight that causal inference from censored data requires structural assumptions on the censoring process beyond the usual independent censoring assumption, which can be represented and verified graphically. Our results establish general non-parametric identifiability and do not rely on particular survival models. We illustrate our proposal with a data example on HPV-testing for cervical cancer screening, where the desired effect is estimated by re-weighted cumulative incidence curves.

翻译：我们考虑连续时间生存或更一般的事件历史情境，其目标在于推断时变治疗过程的因果效应。该效应被形式化为对治疗过程强度进行（可能为假设性的）干预后对结局事件的影响，即一种随机干预。为判断能否从典型的观察性（即非实验性）数据中对干预情境进行有效推断，我们提出了一套图形准则，用于指示通过适当重加权是否足以从观测信息中识别所需因果效应。与已知的因果有向无环图类似，相应的动态图将因果语义与多元计数过程的局部独立模型相结合。重要的是，我们强调从删失数据中进行因果推断需要对删失过程施加超越常规独立删失假设的结构性假设，这些假设可通过图形表示与验证。我们的研究结果建立了普遍的非参数可识别性，且不依赖于特定生存模型。我们以宫颈癌筛查中HPV检测的数据实例说明所提方法，其中通过重加权累积发生率曲线估计所需效应。