Dynamic structural causal models (SCMs) are a powerful framework for reasoning in dynamic systems about direct effects which measure how a change in one variable affects another variable while holding all other variables constant. The causal relations in a dynamic structural causal model can be qualitatively represented with a full-time causal graph. Assuming linearity and causal sufficiency and given the full-time causal graph, the direct causal effect is always identifiable and can be estimated from data by adjusting on any set of variables given by the so-called single-door criterion. However, in many application such a graph is not available for various reasons but nevertheless experts have access to an abstraction of the full-time causal graph which represents causal relations between time series while omitting temporal information. This paper presents a complete identifiability result which characterizes all cases for which the direct effect is graphically identifiable from summary causal graphs and gives two sound finite adjustment sets that can be used to estimate the direct effect whenever it is identifiable.

翻译：动态结构因果模型（SCMs）是在动态系统中推理直接效应的强大框架，该效应衡量在保持所有其他变量不变时，一个变量变化如何影响另一个变量。动态结构因果模型中的因果关系可以用全时因果图进行定性表示。假设线性性和因果充分性，并给定全时因果图，直接因果效应始终可识别，且可通过调整由所谓单扇区准则给出的任意变量集从数据中估计。然而，在许多应用中，由于各种原因此类图并不可得，但专家仍能访问全时因果图的抽象表示，该抽象图描述了时间序列间的因果关系，同时省略了时间信息。本文提出一个完整的可识别性结果，刻画了所有从总结性因果图中可直接效应图形可识别的情形，并给出两个可靠的有限调整集，用于在直接效应可识别时对其进行估计。