Uniform Inference in High-Dimensional Gaussian Graphical Models

TL;DR

This paper develops methods for uniform inference in high-dimensional Gaussian graphical models, enabling reliable estimation and recovery of complex dependency structures even when the number of parameters exceeds the sample size.

Contribution

It introduces uniform inference techniques with fast estimation rates and sparsity guarantees for high-dimensional graphical models, advancing causal structure recovery.

Findings

Uniform estimation rates established under approximate sparsity.

Simulation studies show good small sample performance.

Method enables recovery of complex dependency structures in high dimensions.

Abstract

Graphical models have become a very popular tool for representing dependencies within a large set of variables and are key for representing causal structures. We provide results for uniform inference on high-dimensional graphical models with the number of target parameters $d$ being possible much larger than sample size. This is in particular important when certain features or structures of a causal model should be recovered. Our results highlight how in high-dimensional settings graphical models can be estimated and recovered with modern machine learning methods in complex data sets. To construct simultaneous confidence regions on many target parameters, sufficiently fast estimation rates of the nuisance functions are crucial. In this context, we establish uniform estimation rates and sparsity guarantees of the square-root estimator in a random design under approximate sparsity conditions that might be of independent interest for related problems in high-dimensions. We also demonstrate in a comprehensive simulation study that our procedure has good small sample properties.