An overview of the quantitative causality analysis and causal graph reconstruction based on a rigorous formalism of information flow
arXiv:2112.14839v1
Originality Synthesis-oriented
AI Analysis
It addresses the problem of inferring causal relations from data for AI researchers, but is incremental as it summarizes existing work.
The paper provides an overview of quantitative causality analysis and causal graph reconstruction, summarizing theory and applications developed over 16 years in physics from first principles.
Inference of causal relations from data now has become an important field in artificial intelligence. During the past 16 years, causality analysis (in a quantitative sense) has been developed independently in physics from first principles. This short note is a brief summary of this line of work, including part of the theory and several representative applications.