New technologies have led to vast troves of large and complex data sets across many
scientific domains and industries. People routinely use machine learning techniques not …

Graphical models can represent a multivariate distribution in a convenient and accessible
form as a graph. Causal models can be viewed as a special class of graphical models that …

We discuss the Gaussian graphical model (GGM; an undirected network of partial
correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM …

The advent of large-scale microbiome studies affords newfound analytical opportunities to
understand how these communities of microbes operate and relate to their environment …

In this review, we discuss approaches for learning causal structure from data, also called
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …

We introduce a new approach to functional causal modeling from observational data, called
Causal Generative Neural Networks (CGNN). CGNN leverages the power of neural …

Conditional independence, graphical models and sparsity are key notions for parsimonious
statistical models and for understanding the structural relationships in the data. The theory of …

The fundamental challenge in causal induction is to infer the underlying graph structure
given observational and/or interventional data. Most existing causal induction algorithms …

Deep generative models learn highly complex and non-linear representations to generate
realistic synthetic data. While they have achieved notable success in computer vision and …

Extreme value statistics provides accurate estimates for the small occurrence probabilities of
rare events. While theory and statistical tools for univariate extremes are well developed …