A classification of machine learning approaches for the social and health sciences
Speaker: Anja Leist (Department of Social Sciences; Faculty of Humanities, Education and Social Sciences; University of Luxembourg)
Title: A classification of machine learning approaches for the social and health sciences
Time: Wednesday, 2021.05.05, 10:00 a.m. (CET)
Place: fully virtual (contact Dr. Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
Abstract: The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, among other reasons due to the importance of incorporating domain knowledge into the strategy of data analysis in the social and health sciences. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches. We map established distinctions in data science for the purposes of description, prediction, and causal inference to common research goals. Example applications to predict prison violence, assess prevalence of non-communicable diseases, and explain adverse birth outcomes are presented. The meta-mapping should improve the understanding between computational disciplines and the social and health sciences.