Previous models could not reliably analyze these challenges. Now, researchers have tested 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.
When something bad happens, it is natural to try figure out why it happened. What caused it? If the cause is determined, it may be possible to avoid the same outcome the next time. However, some of the ways in which humans try to understand events, such as resorting to superstition, cannot explain what is actually going on. Neither does correlation, which can only say that event B happened around the same time as event A.
To really know what caused an event, we need to look at causality—how information flows from one event to another. It is the information flow that shows there is a causal link—that event A caused event B. But what happens when the time-sequenced information flow from event A to event B is missing? General causality is required to identify the causes.
Mathematical models for general causality have been very limited, working for up to two causes. Now, in an artificial intelligence breakthrough, researchers have developed the first robust model for general causality that identifies multiple causal connections without time-sequence data, the Multivariate Additive Noise Model (MANM).
Researchers from the University of Johannesburg, South Africa, and National Institute of Technology Rourkela, India, developed the model and tested it on simulated, real-world datasets. The research is published in the journal Neural Networks .
“Uniquely, the model can identify multiple, hierarchical causal factors. It works even if data with time sequencing is not available. The model creates significant opportunities to analyse complex phenomena in areas such as economics, disease outbreaks, climate change and conservation,” says Prof Tshilidzi Marwala, a professor of artificial intelligence, and global