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, 2023 Causal learning has traditionally been regarded as a process of forming epistemically accurate causal beliefs that represent the world. Yet, considering that a primary function of causal learning is to further people’s practical goals by increasing their
Teo Wen Hao Dennis, Desmond C. Ong
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Learning Multiscale Non-stationary Causal Structures [PDF]
, 2023 This paper addresses a gap in the current state of the art by providing a solution for modeling causal relationships that evolve over time and occur at different time scales. Specifically, we introduce the multiscale non-stationary directed acyclic graph
Gianmarco De Francisci Morales +3 more
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Personalizing sustainable agriculture with causal machine learning
, 2023 To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the sustainable intensification of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to ...
Vasileios Sitokonstantinou +3 more
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Definition, methods, and applications of causal machine learning in medicine
Zhongguo gonggong weishengWith therapid advancement of medical science and technology, machine learning has become an integral component of precision medicine. However, traditional machine learning methods often focus on the correlation analysis between independent and outcome ...
Ben NIU, Mengjie WAN, Jue LIU
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Improving the accuracy of medical diagnosis with causal machine learning
Nature Communications, 2020 Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are
Jonathan G. Richens +2 more
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SensorsCausal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focus on
Keyue Yan +5 more
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Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine
IEEE Access, 2021 A feedforward neural network with random weights (RW-FFNN) uses a randomized feature map layer. This randomization enables the optimization problem to be replaced by a standard linear least-squares problem, which offers a major advantage in terms of ...
Akira Sasou
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Water ResearchCoastal marine water quality emerges from complex and dynamic feedbacks between natural processes and human activities, yet disentangling their respective influences remains a persistent challenge.
Han-Wen Zhang +4 more
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Causal Machine Learning for Cost-Effective Allocation of Development Aid [PDF]
Knowledge Discovery and Data MiningThe Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by "leaving no one behind", and, to achieve the SDGs by 2030, poor countries require immense volumes of development aid.
Milan Kuzmanovic +3 more
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Causal Economic Machine Learning (CEML): “Human AI”
AIThis paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory.
Andrew Horton
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