Their marketing material states: "Facilitating machines to go beyond pattern recognition and into cause-and-effect learning". I wonder what they are referring

Their marketing material states: "Facilitating machines to go beyond pattern recognition and into cause-and-effect learning". I wonder what they are referring to. Are they accelerating what SHAP's GradientExplainer [1] does? (namely: crafting inputs at a specific layer, propagating forward to see the influence on class prediction, and sort of backpropagating to pixels) Or is it about something more related to Judea Pearl's work on causality? [1] https://github.com/slundberg/shap#deep-learning-example-with-gradientexplainer-tensorflowkeraspytorch-models https://github.com/slundberg/shap#deep-learning-example-with...