Two of the main techniques used in order to try to discover causal relationships are Graphical Methods (such as Knowledge Graphs and Bayesian Belief Networks) and Explainable AI.
One of the most promising applications of Knowledge Graphs, is to create Machine Learning models able to learn from causality. Knowledge Graph Convolutional Networks (KGCN), represent one of the first successful applications in this ambit.
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