# Inference in high-dimensional graphical models

arXiv: https://arxiv.org/abs/1801.08512 · HTML: https://mlsanity.com/paper/1801.08512/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning](https://mlsanity.com/paper/2106.01474/) (2021)
- [LARGE: A Locally Adaptive Regularization Approach for Estimating Gaussian Graphical Models](https://mlsanity.com/paper/2601.09686/) (2026)
- [Inferential Theory for Granular Instrumental Variables in High Dimensions](https://mlsanity.com/paper/2201.06605/) (2022)
- [Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference](https://mlsanity.com/paper/2604.21843/) (2026)
- [Inference for High-Dimensional Sparse Spectral Precision Matrices](https://mlsanity.com/paper/2606.07986/) (2026)
- [High-dimensional networks and mean squared error for possibly misspecified models](https://mlsanity.com/paper/2608.13171/) (2026)

## Abstract (arXiv)

We provide a selected overview of methodology and theory for estimation and inference on the edge weights in high-dimensional directed and undirected Gaussian graphical models. For undirected graphical models, two main explicit constructions are provided: one based on a global method that maximizes the joint likelihood (the graphical Lasso) and one based on a local (nodewise) method that sequentially applies the Lasso to estimate the neighbourhood of each node. The proposed estimators lead to confidence intervals for edge weights and recovery of the edge structure. We evaluate their empirical performance in an extensive simulation study. The theoretical guarantees for the methods are achieved under a sparsity condition relative to the sample size and regularity conditions. For directed acyclic graphs, we apply similar ideas to construct confidence intervals for edge weights, when the directed acyclic graph is identifiable.