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. Real-world problems in economics and public health can be very hard to analyse. Often, multiple causes are suspected but large datasets with time-sequenced data are not available. Previous models could not reliably analyse these challenges. Now researchers have tested the first Artificial Intelligence model to identify and rank many causes in real-world problems, built on the concepts of Causal Influence.
See more at https://www.uj.ac.za/newandevents/Pages/Causality.aspx
Where Prof Tshilidzi Marwala and Dr Pramod Kumar Parida from the University of Johannesburg share insights about the research, which is published in the journal Neural Networks
UCOd76GJ46qAKZxRly8F7snQ
source
