This paper considers how to classify the effects of interventions in causal models for outcomes and exposures observed over time. First, we demonstrate the limitations of the most common uses of potential outcomes and causal directed acyclic graphs for capturing all possible interventions in a time varying framework, particularly in problems where the key question concerns interventions to maintain or change equilibrium behaviour. Second, we adopt a system and state based approach rather than a measurement-based approach to identify the causal parameters. In particular, we discuss how assumptions about the system's equilibrium and the effects of interventions on that equilibrium can allow for more specific causal interpretations and clarify the goals of design and analysis. Third, we show how the ability to identify the the causal parameters of a time varying system depends on the selection of timepoints for measuring the system's states. We address this by proposing a novel version of the null effect, which is designed to distinguish between transient and lasting causal effects.

翻译：本文探讨如何对随时间观测的结果和暴露变量进行因果模型中的干预效应分类。首先，我们展示了在时变框架中，潜在结果和因果有向无环图在捕获所有可能干预方面的常见用法的局限性，尤其是在关键问题涉及维持或改变均衡行为的干预场景中。其次，我们采用基于系统与状态的方法而非基于测量的方法来识别因果参数。具体而言，我们讨论了关于系统均衡的假设以及干预对该均衡的影响如何能够实现更具体的因果解释，并阐明设计与分析的目标。第三，我们展示了时变系统因果参数的可识别性如何依赖于系统状态测量时间点的选择。为此，我们提出了一种新型零效应定义，旨在区分瞬时因果效应与持久因果效应。