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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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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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Do capuchin monkeys (Cebus apella) diagnose causal relations in the absence of a direct reward?
PLoS ONE, 2014 We adapted a method from developmental psychology to explore whether capuchin monkeys (Cebus apella) would place objects on a "blicket detector" machine to diagnose causal relations in the absence of a direct reward.
Brian J Edwards +9 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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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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Estimating causal effects with machine learning: A guide for ecologists
Methods in Ecology and EvolutionIn ecology, there is a growing need to move beyond correlations to uncovering causal effects from observational data. With the parallel increase in big data and machine learning algorithms, the opportunity now exists to benefit from causal machine ...
Suchinta Arif
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Evaluating the Role of Machine Learning in Economics: A Cutting-Edge Addition or Rhetorical Device?
Studies in Logic, Grammar and Rhetoric, 2023 This paper explores the integration of machine learning into economics and social sciences, assessing its potential impact and limitations. It introduces fundamental machine learning concepts and principles, highlighting the differences between the two ...
Czech Sławomir
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Data & PolicyCausal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, that is, there is no ...
Patrick Rehill, Nicholas Biddle
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Artificial intelligence and machine learning in emergency medicine: a narrative review
Acute Medicine & Surgery, 2022 Aim The emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality ...
Brianna Mueller +4 more
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Machine learning in causal inference for epidemiology [PDF]
European Journal of EpidemiologyAbstractIn causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimates.
Moccia, Chiara +7 more
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