An overview of the quantitative causality analysis and causal graph reconstruction based on a rigorous formalism of information flow
Systems and Control
2022-01-03 v1 Artificial Intelligence
Systems and Control
Data Analysis, Statistics and Probability
Abstract
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.
Keywords
Cite
@article{arxiv.2112.14839,
title = {An overview of the quantitative causality analysis and causal graph reconstruction based on a rigorous formalism of information flow},
author = {X. San Liang},
journal= {arXiv preprint arXiv:2112.14839},
year = {2022}
}
Comments
7 pages, 1 figure. Presented at the First International AIxIA Workshop on Causality, Causal-ITALY, Italian Conference on Artificial Intelligence, November 30, 2021