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）是用于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，一个变量的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借助全时因果图进行定性表示。在线性性、因果充分性假设下，且给定全时因果图时，直接因果效应总是可识别的，并可通过调整任意满足所谓单扇区准则的变量集，从数据中估计得出。然而，在许多应用中，由于各种原因，此类图并不可得；但专家仍可获取全时因果图的抽象表示——该抽象图在忽略时间信息的前提下，描述了时间序列间的因果关系。本文提出一种完整的可识别性结果，刻画了从摘要因果图中可通过图形方式识别直接效应的所有情形，并给出了两个有效的有限调整集，当直接效应可识别时，可用于估计该效应。