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Quantitative causality, causality-guided scientific discovery, and causal machine learning
It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization.
An overview of the quantitative causality analysis and causal graph reconstruction based on a rigorous formalism of information flow
Inference of causal relations from data now has become an important field in artificial intelligence.
The causal interaction between the subnetworks of a complex network
This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a large-dimensional parental system, in order to investigate problems such as the effective connectivity between two brain regions, each with millions of neurons involved.
Normalized multivariate time series causality analysis and causal graph reconstruction
While it seems to be a huge challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum.
