Gaussian Graphical models (GGM) are widely used to estimate the network structures in many applications ranging from biology finance. In practice, data is often corrupted by latent confounders which biases inference of underlying true graphical structure. this paper, we compare and contrast two strategies for with confounders: variables (LVGGM) PCA-based removal confounding (PCA+GGM). While the...