A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

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 …

Causal disentanglement aims to uncover a representation of data using latent variables that
are interrelated through a causal model. Such a representation is identifiable if the latent …

Information geometry is the differential geometric treatment of statistical models. It thereby
provides the mathematical foundation of statistics. Information geometry therefore is of …

We develop Markov categories as a framework for synthetic probability and statistics,
following work of Golubtsov as well as Cho and Jacobs. This means that we treat the …

Higher order elicitability and Osband's principle Page 1 The Annals of Statistics 2016, Vol. 44,
No. 4, 1680–1707 DOI: 10.1214/16-AOS1439 © Institute of Mathematical Statistics, 2016 …

Bayesian Networks: An Introduction provides a self-contained introduction to the theory and
applications of Bayesian networks, a topic of interest and importance for statisticians …

How does an algebraic geometer studying secant varieties further the understanding of
hypothesis tests in statistics? Why would a statistician working on factor analysis raise open …

This paper considers inference of causal structure in a class of graphical models called
conditional DAGs. These are directed acyclic graph (DAG) models with two kinds of …

The problem of causal inference is to determine if a given probability distribution on
observed variables is compatible with some causal structure. The difficult case is when the …