含潜在混杂因子的时间序列的因果祖先图表征
Characterization of causal ancestral graphs for time series with latent confounders
AI总结：
该研究提出一类用于含未观测混杂因子的多变量时间序列的新型因果图模型，证明其为现有模型类的真子集，可在无额外假设下得出更强因果推断，且其马尔可夫等价类图形表示蕴含更丰富的因果知识。
摘要：
本文提出了一类新型图模型，用于表示含未观测混杂因子的多变量时间序列的时滞特异性因果关系与独立性。我们完整表征了这类图，并证明它们是当前所用模型类的真子集。研究表明，无需额外假设，就能从这类新型图中得出更强的因果推断。我们还提出了新型图的马尔可夫等价类的图形表示，该表示包含的因果知识比当前最先进的因果发现算法所学习到的更丰富。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
