Cognitive neuroscientists habitually deny that functional neuroimaging can furnish causal information about the relationship between brain events and behavior. However, imaging st...
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
Current methods for causal structure learning tend to be computationally intensive or intractable for large datasets. Some recent approaches have speeded up the process by first m...
: This review presents empirical researchers with recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional stati...
Directed acyclic graph (DAG) models are popular tools for describing causal relationships and for guiding attempts to learn them from data. In particular, they appear to supply a ...
We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets. The algorithm outputs a graph with edges correspo...
Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G...
Versioning file systems provide the ability to recover from a variety of failures, including file corruption, virus and worm infestations, and user mistakes. However, using versio...
The principle of causation is fundamental to science and society and has remained an active topic of discourse in philosophy for over two millennia. Modern philosophers often rely...
■ Knowledge about cause and effect relationships (e.g., virus– epidemic) is essential for predicting changes in the environment and for anticipating the consequences of events...
Daniela B. Fenker, Mircea Ariel Schoenfeld, Michae...
Causality is an important aspect of how we construct reality. Yet, while many psychological phenomena have been studied in their relation to virtual reality (VR), very little work...