CHARACTERIZATION OF CAUSAL ANCESTRAL GRAPHS FOR TIME SERIES WITH LATENT CONFOUNDERS

• 成果类型: Article
• 署名作者: Gerhardus, Andreas
• 署名单位: Helmholtz Association; German Aerospace Centre (DLR)
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/23-AOS2325
• 发表日期: 2024
• 页码: 103-130
• 关键词: markov equivalence; completeness; INDEPENDENCE; DISCOVERY; models

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.