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

arXiv: https://arxiv.org/abs/2112.14839 · HTML: https://mlsanity.com/paper/2112.14839/

## 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.

## Builds on (most cited first, 3 of 3)

- [From Ordinary Differential Equations to Structural Causal Models: the deterministic case](https://mlsanity.com/paper/1408.2063/) (2014)
- [Detecting confounding in multivariate linear models via spectral analysis](https://mlsanity.com/paper/1704.01430/) (2017)
- [Normalized multivariate time series causality analysis and causal graph reconstruction](https://mlsanity.com/paper/2104.11360/) (2021)

## 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.