Ancestral Graph

Ancestral graphs are graphical models used to represent causal relationships between variables, particularly in the presence of latent (unobserved) confounders. Current research focuses on developing efficient algorithms for learning these graphs from data, including score-based searches, reinforcement learning approaches for constructing ancestral recombination graphs, and methods incorporating expert knowledge to refine model structures. These advancements are improving causal discovery accuracy and enabling applications such as machine unlearning and more robust causal inference in time series analysis, ultimately contributing to a deeper understanding of complex systems.