An overview of the quantitative causality analysis and causal graph reconstruction based on a rigorous formalism of information flow

2021/12/31 by Liang, X. San
#Artificial Intelligence (cs.AI) #Data Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Statistics and Probability (physics.data-an) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2112.14839

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.

Related

• Measuring Causality: The Science of Cause and Effect
• Causality for Machine Learning
• From Statistical to Causal Learning
• Towards Causal Representation Learning
• Toward Causal Representation Learning