In this paper, we introduce a novel class of graphical models for representing time lag specific causal relationships and independencies of multivariate time series with unobserved confounders. We completely characterize these graphs and show that they constitute proper subsets of the currently employed model classes. As we show, from the novel graphs one can thus draw stronger causal inferences -- without additional assumptions. We further introduce a graphical representation of Markov equivalence classes of the novel graphs. This graphical representation contains more causal knowledge than what current state-of-the-art causal discovery algorithms learn.

翻译：本文提出了一类新颖的图模型，用于表示包含未观测混杂因素的多变量时间序列中具有时滞特定性的因果关系与独立性。我们对此类图进行了完整刻画，并证明它们构成了当前常用模型类的真子集。研究表明，无需额外假设，从这些新颖图模型中即可得出更强的因果推断。此外，我们进一步引入了此类图模型马尔可夫等价类的图形表示。相较于当前最先进的因果发现算法所习得的结果，该图形表示蕴含了更丰富的因果知识。