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

arXiv 2112.14839 ↗First posted 31 Dec 2021eess.SY cs.AI cs.SYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 31 references that are in the map. First posted on arXiv 31 Dec 2021.

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References · 3

  1. 2014From Ordinary Differential Equations to Structural Causal Models: the deterministic casecited by 41
  2. 2017Detecting confounding in multivariate linear models via spectral analysiscited by 14
  3. 2021Normalized multivariate time series causality analysis and causal graph reconstructioncited by 7

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