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From Dependency to Causality: A Machine Learning Approach

  • Gianluca BontempiEmail author
  • Maxime Flauder
Chapter
Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)

Abstract

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.

Keywords

Causal inference Information theory Machine learning 

Notes

Acknowledgements

This work was supported by the ARC project “Discovery of the molecular pathways regulating pancreatic beta cell dysfunction and apoptosis in diabetes using functional genomics and bioinformatics” funded by the Communauté Française de Belgique and the BridgeIRIS project funded by INNOVIRIS, Brussels Region. The authors wishes to thank the editor and the anonymous reviewers for their insightful comments and remarks.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Machine Learning Group, Computer Science DepartmentULB, Université Libre de BruxellesBrusselsBelgium

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