Characterization of causal ancestral graphs for time series with latent confounders
提出一类新图模型来刻画含未观测混杂因素的多变量时间序列中时滞特定的因果关系和独立性，并证明其能比现有模型得出更强的因果推断，无需额外假设。
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.