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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Silva, R. (2017). Causality. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_36
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_36
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