CAUSAL DISCOVERY IN MACHINE LEARNING: THEORIES AND APPLICATIONS

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Nogueira, AR (Author) Other; João Gama (Author) FEP; Ferreira, CA (Author) Other

Journal

Title: Journal of Dynamics and Games

Vol. 8

Pages: 203-231

Publisher: American Institute of Mathematical Sciences

Indexing

ISI Web of Knowledge - 21 Citations

Scopus - 30 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 29