Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

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 …

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 …

Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …

Cognition and behavior emerge from brain network interactions, such that investigating
causal interactions should be central to the study of brain function. Approaches that …

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

This article presents an overview of several known approaches to causal discovery. It is
organized by relating the different fundamental assumptions that the methods depend on …

Complex systems are challenging to understand, especially when they defy manipulative
experiments for practical or ethical reasons. Several fields have developed parallel …

Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …