Lamsaf Asmae, Carrilho Rui, Neves João C, Proença Hugo
IT: Instituto de Telecomunicações, University of Beira Interior, 6200-001 Covilhã, Portugal.
Department of Computer Science, University of Beira Interior, 6200-209 Covilhã, Portugal.
Sensors (Basel). 2025 Apr 9;25(8):2373. doi: 10.3390/s25082373.
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.
因果关系涉及区分原因和结果，对于理解数据中的复杂关系至关重要。本文对两个关键领域的因果关系进行了综述：因果发现和因果推断。因果发现将数据转换为图形结构，以说明变量如何相互影响，而因果推断则量化这些变量对目标结果的影响。通过将因果推理集成到机器学习中，模型更加稳健和准确，改善了预测和分类等应用。我们介绍了用于检测因果关系的各种方法，以及如何将这些方法应用于选择或提取相关特征，特别是从传感器数据集中提取。当因果关系用于特征选择时，它支持诸如故障检测、异常检测和预测性维护等应用，这些应用对于复杂系统的维护至关重要。传统的基于相关性的特征选择方法往往忽略了重要的因果联系，导致见解不完整。我们的研究强调了如何集成因果关系，从而实现更强、更深入的特征选择，并最终在机器学习任务中实现更好的决策。
