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 …

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 network reconstruction from time series is an emerging topic in many fields of
science. Beyond inferring directionality between two time series, the goal of causal network …

Learning a causal directed acyclic graph from data is a challenging task that involves
solving a combinatorial problem for which the solution is not always identifiable. A new line …

Discovering causal structure among a set of variables is a fundamental problem in many
empirical sciences. Traditional score-based casual discovery methods rely on various local …

Understanding the laws that govern a phenomenon is the core of scientific progress. This is
especially true when the goal is to model the interplay between different aspects in a causal …

An important goal common to domain adaptation and causal inference is to make accurate
predictions when the distributions for the source (or training) domain (s) and target (or test) …

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 …

Practitioners in diverse fields such as healthcare, economics and education are eager to
apply machine learning to improve decision making. The cost and impracticality of …

Prerequisites There is one main prerequisite: basic probability. This course assumes you've
taken an introduction to probability course or have had equivalent experience. Topics from …