Characterization of causal ancestral graphs for time series with latent confounders

2024/02/01 by Andreas Gerhardus
Computer Science · #Bayesian Modeling and Causal Inference #Error Correcting Code Techniques #Reinforcement Learning in Robotics

paper · doi:10.1214/23-aos2325

Abstract

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.

Citations

• On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods

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• Characterization of causal ancestral graphs for time series with latent confounders
• Causal Identification in Time Series Models
• Inferring extended summary causal graphs from observational time series
• From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
• Causal Reasoning in Graphical Time Series Models