Bayesian Networks are probabilistic graphical models that represent conditional
independence relationships among variables as a directed acyclic graph (DAG), where …

Many algorithms for inferring causality rely heavily on the faithfulness assumption. The main
justification for imposing this assumption is that the set of unfaithful distributions has …

In this paper we give a review of recent causal inference methods. First, we discuss methods
for causal structure learning from observational data when confounders are not present and …

This paper considers conducting inference about the effect of a treatment (or exposure) on
an outcome of interest. In the ideal setting where treatment is assigned randomly, under …

We consider the problem of estimating the differences between two causal directed acyclic
graph (DAG) models with a shared topological order given iid samples from each model …

We prove that $\Omega (s\log p) $ samples suffice to learn a sparse Gaussian directed
acyclic graph (DAG) from data, where $ s $ is the maximum Markov blanket size. This …

Spirtes, Glymour and Scheines [Causation, Prediction, and Search (1993) Springer]
described a pointwise consistent estimator of the Markov equivalence class of any causal …

We study a family of regularized score-based estimators for learning the structure of a
directed acyclic graph (DAG) for a multivariate normal distribution from high-dimensional …

Algebro-geometric methods have proven to be very successful in the study of graphical
models in statistics. In this paper we introduce the foundations to carry out a similar study of …

Markov models lie at the interface between statistical independence in a probability
distribution and graph separation properties. We review model selection and estimation in …