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Encyclopedia of Machine Learning and Data Mining pp 1–9Cite as

Causality

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Abstract

Causality is an essential concept in our understanding of the world as, in order to predict how a system behaves under an intervention, it is necessary to have causal knowledge of the impact of interventions. This knowledge should be expressed in a language built on top of probabilistic models, since the axioms of probability do not provide a way of expressing how external interventions affect a system. Learning this knowledge from data also poses additional challenges compared to the standard machine learning problem, as much data comes from passive observations that do not follow the same regime under which our predictions will take place.

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Author information

Authors and Affiliations

  1. Centre for Computational Statistics and Machine Learning, University College London, London, UK

    Ricardo Silva

Authors
  1. Ricardo Silva
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Correspondence to Ricardo Silva .

Editor information

Editors and Affiliations

  1. Engineering (CSE), University of New South Wales School of Computer Science &, Sydney, New South Wales, Australia

    Claude Sammut

  2. Software Engineering, Monash University School of Computer Science &, Melbourne, Victoria, Australia

    Geoffrey I. Webb

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Silva, R. (2016). Causality. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_36-1

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  • DOI: https://doi.org/10.1007/978-1-4899-7502-7_36-1

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  • Publisher Name: Springer, Boston, MA

  • Online ISBN: 978-1-4899-7502-7

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