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 …

Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …

We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …

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 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 …

This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …

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 …

What is the difference between a prediction that is made with a causal model and that with a
non-causal model? Suppose that we intervene on the predictor variables or change the …

Learning graphical structure based on Directed Acyclic Graphs (DAGs) is a challenging
problem, partly owing to the large search space of possible graphs. A recent line of work …

We revisit the structure learning problem for dynamic Bayesian networks and propose a
method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter …