When something bad happens to a person, it is human to try figure out why it happened. What caused it? If we understand that, it may be possible to avoid the same outcome the next time. However, some of the ways that we use to try to understand events, such as superstition, cannot explain what is actually going on.
Researchers from the University of Johannesburg and National Institute of Technology Rourkela, India, have tested a model of General Causality on simulated real-world data sets. The model is the first Artificial Intelligence model to identify and rank many causes in real-world problems without time-sequenced data, using a multi-nodal causal structure and Directed Acyclic Graphs (DAGs).