Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

Causal learning is a key challenge in scientific artificial intelligence as it allows researchers
to go beyond purely correlative or predictive analyses towards learning underlying cause …

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 consider the problem of learning causal information between random variables in
directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection …

When domain knowledge is limited and experimentation is restricted by ethical, financial, or
time constraints, practitioners turn to observational causal discovery methods to recover the …

The gold standard for discovering causal relations is by means of experimentation. Over the
last decades, alternative methods have been proposed that can infer causal relations …

Causal discovery becomes especially challenging when the possibility of latent confounding
and/or selection bias is not assumed away. For this task, ancestral graph models are …

Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …