Characterization of causal ancestral graphs for time series with latent confounders
This work addresses the problem of causal discovery in time series with unobserved confounders for researchers in statistics and machine learning, offering incremental improvements in graphical representation.
The authors introduced a new class of graphical models for time series with latent confounders, enabling stronger causal inferences without extra assumptions, and showed these graphs are proper subsets of existing models.
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.