Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.
No takes yet. Share an insight, caveat, or question.
Susan Athey (2017) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: