Design of Experiment for Discovering Directed Mixed Graph
This research develops an experimental design framework to accurately identify Directed Mixed Graphs (DMGs), which contain both feedback loops and unobserved confounders. Traditional methods fail here because cycles complicate standard conditional independence tests. The proposed approach uses a three-step algorithm involving observational data analysis, SCC identification, and specific separation rules to recover the full causal structure.