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Causality, Machine Learning, and Feature Selection: A Survey

Causality, which involves distinguishing between cause and effect, is essential for understanding complex relationships in data. This paper provides a review of causality in two key areas: causal discovery and causal inference. Causal discovery transforms data into graphical structures that illustra...

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Principais autores: Asmae Lamsaf, Rui Carrilho, João C. Neves, Hugo Proença
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2025-04-01
Serier:Sensors
Fag:
causality
causal inference
causal discovery
machine learning
feature selection
feature extraction
Online adgang:https://www.mdpi.com/1424-8220/25/8/2373
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