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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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2373 |
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| author | Asmae Lamsaf Rui Carrilho João C. Neves Hugo Proença |
| author_facet | Asmae Lamsaf Rui Carrilho João C. Neves Hugo Proença |
| author_sort | Asmae Lamsaf |
| collection | DOAJ |
| description | 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 illustrate how variables influence one another, while causal inference quantifies the impact of these variables on a target outcome. The models are more robust and accurate with the integration of causal reasoning into machine learning, improving applications like prediction and classification. We present various methods used in detecting causal relationships and how these can be applied in selecting or extracting relevant features, particularly from sensor datasets. When causality is used in feature selection, it supports applications like fault detection, anomaly detection, and predictive maintenance applications critical to the maintenance of complex systems. Traditional correlation-based methods of feature selection often overlook significant causal links, leading to incomplete insights. Our research highlights how integrating causality can be integrated and lead to stronger, deeper feature selection and ultimately enable better decision making in machine learning tasks. |
| format | Article |
| id | doaj-art-fa7d3147226d44f4a8d6c386fe27f985 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-fa7d3147226d44f4a8d6c386fe27f9852025-08-20T03:13:59ZengMDPI AGSensors1424-82202025-04-01258237310.3390/s25082373Causality, Machine Learning, and Feature Selection: A SurveyAsmae Lamsaf0Rui Carrilho1João C. Neves2Hugo Proença3IT: Instituto de Telecomunicações, University of Beira Interior, 6200-001 Covilhã, PortugalIT: Instituto de Telecomunicações, University of Beira Interior, 6200-001 Covilhã, PortugalDepartment of Computer Science, University of Beira Interior, 6200-209 Covilhã, PortugalIT: Instituto de Telecomunicações, University of Beira Interior, 6200-001 Covilhã, PortugalCausality, 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 illustrate how variables influence one another, while causal inference quantifies the impact of these variables on a target outcome. The models are more robust and accurate with the integration of causal reasoning into machine learning, improving applications like prediction and classification. We present various methods used in detecting causal relationships and how these can be applied in selecting or extracting relevant features, particularly from sensor datasets. When causality is used in feature selection, it supports applications like fault detection, anomaly detection, and predictive maintenance applications critical to the maintenance of complex systems. Traditional correlation-based methods of feature selection often overlook significant causal links, leading to incomplete insights. Our research highlights how integrating causality can be integrated and lead to stronger, deeper feature selection and ultimately enable better decision making in machine learning tasks.https://www.mdpi.com/1424-8220/25/8/2373causalitycausal inferencecausal discoverymachine learningfeature selectionfeature extraction |
| spellingShingle | Asmae Lamsaf Rui Carrilho João C. Neves Hugo Proença Causality, Machine Learning, and Feature Selection: A Survey Sensors causality causal inference causal discovery machine learning feature selection feature extraction |
| title | Causality, Machine Learning, and Feature Selection: A Survey |
| title_full | Causality, Machine Learning, and Feature Selection: A Survey |
| title_fullStr | Causality, Machine Learning, and Feature Selection: A Survey |
| title_full_unstemmed | Causality, Machine Learning, and Feature Selection: A Survey |
| title_short | Causality, Machine Learning, and Feature Selection: A Survey |
| title_sort | causality machine learning and feature selection a survey |
| topic | causality causal inference causal discovery machine learning feature selection feature extraction |
| url | https://www.mdpi.com/1424-8220/25/8/2373 |
| work_keys_str_mv | AT asmaelamsaf causalitymachinelearningandfeatureselectionasurvey AT ruicarrilho causalitymachinelearningandfeatureselectionasurvey AT joaocneves causalitymachinelearningandfeatureselectionasurvey AT hugoproenca causalitymachinelearningandfeatureselectionasurvey |