CAUSAL DISCOVERY IN MACHINE LEARNING: THEORIES AND APPLICATIONS

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Nogueira, AR (Autor) Outra; João Gama (Autor) FEP; Ferreira, CA (Autor) Outra

Revista

Título: Journal of Dynamics and Games

Vol. 8

Páginas: 203-231

Editora: American Institute of Mathematical Sciences

Indexação

ISI Web of Knowledge - 21 Citações

Scopus - 30 Citações

Outras Informações

ID Authenticus: P-00T-RXA

DOI: 10.3934/jdg.2021008

Abstract (EN): Determining the cause of a particular event has been a case of study for several researchers over the years. Finding out why an event happens (its cause) means that, for example, if we remove the cause from the equation, we can stop the effect from happening or if we replicate it, we can create the subsequent effect. Causality can be seen as a mean of predicting the future, based on information about past events, and with that, prevent or alter future outcomes. This temporal notion of past and future is often one of the critical points in discovering the causes of a given event. The purpose of this survey is to present a cross-sectional view of causal discovery domain, with an emphasis in the machine learning/data mining area.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 29