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 …

Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

A fundamental task in various disciplines of science, including biology, is to find underlying
causal relations and make use of them. Causal relations can be seen if interventions are …

Abstract Machine learning is a branch of computer science that has the potential to transform
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …

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 …

This paper aims to give a broad coverage of central concepts and principles involved in
automated causal inference and emerging approaches to causal discovery from iid data and …

The discovery of causal relationships from purely observational data is a fundamental
problem in science. The most elementary form of such a causal discovery problem is to …

We consider the problem of learning causal directed acyclic graphs from an observational
joint distribution. One can use these graphs to predict the outcome of interventional …

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 …

Structural equation models and Bayesian networks have been widely used to analyze
causal relations between continuous variables. In such frameworks, linear acyclic models …