# From dependency to causality: a machine learning approach

arXiv: https://arxiv.org/abs/1412.6285 · HTML: https://mlsanity.com/paper/1412.6285/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2014.

## Built on by (most cited first, 4 of 4)

- [ML4C: Seeing Causality Through Latent Vicinity](https://mlsanity.com/paper/2110.00637/) (2021)
- [SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions](https://mlsanity.com/paper/2410.18416/) (2024)
- [Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles](https://mlsanity.com/paper/2501.02672/) (2025)
- [Causal Discovery in Multivariate Time Series through Mutual Information Featurization](https://mlsanity.com/paper/2508.01848/) (2025)

## Abstract (arXiv)

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning approach to infer the existence of a directed causal link between two variables in multivariate settings with n>2 variables. The approach relies on the asymmetry of some conditional (in)dependence relations between the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for n>2 variate distributions.