Causal discovery with ancestral graphs - Hu, Z (2023)
Graphical models serve as a visual representation that captures the underlying conditional independence relationships within distributions, employing either directed or undirected graphs. In this thesis, we explore maximal ancestral graphs (MAGs), which is an extension to the conventionaldirected acyclic graphs (DAGs). While DAGs excel in illustrating causal relationships, they fail to capture all the conditional independences on the margin in the absence of latent confounders and selection bias. MAGs provide a more comprehensive depiction of complex dependencies by encompassing both direct causal connections and indirect influences stemming from latent variables and selection bias.