https://www.sciencedaily.com/releases/2018/06/180605112115.htm

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? Now we need general causality to identify the causes.

Mathematical models for general causality have been very limited, working for up to two causes. Now in a huge Artificial Intelligence breakthrough, researchers have developed the first robust model for general causality which identifies multiple causal connections without time-sequence data: a Multivariate Additive Noise Model (MANM).