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 …

Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …

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 …

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

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 …

Learning graphical conditional independence structures is an important machine learning
problem and a cornerstone of causal discovery. However, the accuracy and execution time …

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

Cognition and behavior emerge from brain network interactions, suggesting that causal
interactions should be central to the study of brain function. Yet, approaches that …

Human brain effective connectivity characterizes the causal effects of neural activities
among different brain regions. Studies of brain effective connectivity networks (ECNs) for …

Whereas probabilistic models describe the dependence structure between observed
variables, causal models go one step further: They predict, for example, how cognitive …