Many research questions in Earth and environmental sciences are inherently causal,
requiring robust analyses to establish whether and how changes in one variable cause …

Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

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 …

Causal disentanglement aims to uncover a representation of data using latent variables that
are interrelated through a causal model. Such a representation is identifiable if the latent …

Abstract Background Personalized, precision, P4, or stratified medicine is understood as a
medical approach in which patients are stratified based on their disease subtype, risk …

Manipulation is a concern in many domains, such as social media, advertising, and
chatbots. As AI systems mediate more of our digital interactions, it is important to understand …

A systematic, innovative introduction to the field of network analysis, Network Psychometrics
with R: A Guide for Behavioral and Social Scientists provides a comprehensive overview of …

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 …

Causal learning is a key challenge in scientific artificial intelligence as it allows researchers
to go beyond purely correlative or predictive analyses towards learning underlying cause …

On time-series data, most causal discovery methods fit a new model whenever they
encounter samples from a new underlying causal graph. However, these samples often …