Learning Gaussian Graphical Models with Latent Confounders

TL;DR

This paper compares two strategies for inferring Gaussian Graphical Models with latent confounders, introduces a new combined method, and provides theoretical guarantees and practical evaluations.

Contribution

It introduces a novel method combining LVGGM and PCA+GGM, with theoretical analysis and practical validation for better confounder handling.

Findings

The new method outperforms individual approaches in simulations.

Theoretical guarantees are established for the PCA-based method.

Guidelines are provided for choosing the appropriate method based on data.

Abstract

Gaussian Graphical models (GGM) are widely used to estimate the network structures in many applications ranging from biology to finance. In practice, data is often corrupted by latent confounders which biases inference of the underlying true graphical structure. In this paper, we compare and contrast two strategies for inference in graphical models with latent confounders: Gaussian graphical models with latent variables (LVGGM) and PCA-based removal of confounding (PCA+GGM). While these two approaches have similar goals, they are motivated by different assumptions about confounding. In this paper, we explore the connection between these two approaches and propose a new method, which combines the strengths of these two approaches. We prove the consistency and convergence rate for the PCA-based method and use these results to provide guidance about when to use each method. We demonstrate the effectiveness of our methodology using both simulations and in two real-world applications.