  The uptake of machine learning (ML) approaches in the social and health
sciences has been rather slow, and research using ML for social and health
research questions remains fragmented. This may be due to the separate
development of research in the computational/data versus social and health
sciences as well as a lack of accessible overviews and adequate training in ML
techniques for non data science researchers. This paper provides a 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, and causal inference to common research goals, such as estimating
prevalence of adverse health or social outcomes, predicting the risk of an
event, and identifying risk factors or causes of adverse outcomes. This
meta-mapping aims at overcoming disciplinary barriers and starting a fluid
dialogue between researchers from the social and health sciences and
methodologically trained researchers. Such mapping may also 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.
