Uniform Inference in High-Dimensional Gaussian Graphical Models
This work addresses the challenge of recovering causal structures in high-dimensional settings for researchers in statistics and machine learning, representing an incremental advancement in inference methods.
The paper tackles the problem of performing uniform inference on high-dimensional Gaussian graphical models where the number of parameters can exceed the sample size, establishing uniform estimation rates and sparsity guarantees for the square-root estimator under approximate sparsity conditions, with simulation results showing good small-sample performance.
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.