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 an acyclic full-time causal graph. Assuming linearity and no hidden confounding and given the full-time causal graph, the direct causal effect is always identifiable. However, in many application such a graph is not available for various reasons but nevertheless experts have access to the summary causal graph of the full-time causal graph which represents causal relations between time series while omitting temporal information and allowing cycles. This paper presents a complete identifiability result which characterizes all cases for which the direct effect is graphically identifiable from a summary causal graph 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)是推理动态系统中直接效应的强大框架，该效应衡量在保持其他变量不变时，一个变量变化对另一变量的影响。动态结构因果模型中的因果关系可通过无环全时因果图进行定性表示。假设线性关系且无隐藏混杂因素，给定全时因果图时，直接因果效应总是可识别的。然而，在许多实际应用中，由于各种原因无法获得此类图，但领域专家通常可获取全时因果图的摘要因果图——该图表示时间序列间的因果关系，同时省略时序信息并允许存在环状结构。本文提出完整的可辨识性结果，刻画了从摘要因果图中通过图形方式识别直接效应的所有情形，并给出两个可靠的有限调整集，可在直接效应可识别时用于估计该效应。