Selective Inference in Graphical Models via Maximum Likelihood
The graphical lasso is a widely used algorithm for fitting undirected Gaussian graphical models. However, for inference on functionals of edge values in the learned graph, standard tools lack formal statistical guarantees, such as control of the type I error rate. We introduce a selective inference method for asymptotically valid inference after graphical lasso selection with added randomization. We obtain a selective likelihood, conditional on the event of selection, through a change of variable on the known density of the randomization variables. Our method enables interval estimation and hypothesis testing for a wide range of functionals of edge values in the learned graph using the conditional maximum likelihood estimator. Our numerical studies show that introducing a small amount of randomization: (i) achieves better results than data splitting in terms of model selection quality; (ii) ensures intervals of bounded length also in high-dimensional settings where data splitting suffers from low power or does not reach asymptotic regime due to limited samples for inference; (iii) enables inference for a wide range of inferential targets in the learned graph, including partial correlations and measures of node influence and connectivity between nodes.