Animal models of threat processing have evolved beyond the amygdala to incorporate a
distributed neural network. In human research, evidence has intensified in recent years to …

When extreme, anxiety can become debilitating. Anxiety disorders, which often first emerge
early in development, are common and challenging to treat, yet the underlying mechanisms …

In this essay I discuss potential outcome and graphical approaches to causality, and their
relevance for empirical work in economics. I review some of the work on directed acyclic …

We describe a formal approach based on graphical causal models to identify the" root
causes" of the change in the probability distribution of variables. After factorizing the joint …

We test the adequacies of several proposed and two new statistical methods for recovering
the causal structure of systems with feedback from synthetic BOLD time series. We compare …

Functional magnetic resonance imaging (fMRI) has become one of the most common
imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have …

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

Causal abstraction provides a theory describing how several causal models can represent
the same system at different levels of detail. Existing theoretical proposals limit the analysis …

Emotions play a central role in human experience. Over time, methods for manipulating
emotion have become increasingly refined and techniques for making sense of the …

Neuroscientific studies aim to find an accurate and reliable brain Effective Connectome
(EC). Although current EC discovery methods have contributed to our understanding of brain …