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• W4306855755 abstract "Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research." @default.
• W4306855755 created "2022-10-20" @default.
• W4306855755 creator A5027163198 @default.
• W4306855755 creator A5049640253 @default.
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• W4306855755 creator A5068815897 @default.
• W4306855755 creator A5071664854 @default.
• W4306855755 creator A5078801518 @default.
• W4306855755 creator A5081540617 @default.
• W4306855755 date "2022-10-21" @default.
• W4306855755 modified "2023-10-04" @default.
• W4306855755 title "Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences" @default.
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