# Measuring Causality: The Science of Cause and Effect

arXiv: https://arxiv.org/abs/1910.08750 · HTML: https://mlsanity.com/paper/1910.08750/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Simplifying Causality: A Brief Review of Philosophical Views and Definitions with Examples from Economics, Education, Medicine, Policy, Physics and Engineering](https://mlsanity.com/paper/2212.13537/) (2022)

## Abstract (arXiv)

Determining and measuring cause-effect relationships is fundamental to most scientific studies of natural phenomena. The notion of causation is distinctly different from correlation which only looks at association of trends or patterns in measurements. In this article, we review different notions of causality and focus especially on measuring causality from time series data. Causality testing finds numerous applications in diverse disciplines such as neuroscience, econometrics, climatology, physics and artificial intelligence.