- (30 min)
Misuses of Machine Learning in Health Policy
We highlight some common (and costly) reasons for misuse of machine learning in health, illustrated using the potential outcomes framework from econometric work on causal inference. First, the failure to specify the decision which will be influenced by the prediction: the same prediction can lead to valid inferences for certain decisions but highly suspect ones for other decisions. Second, the selective labels problem: the data used to form the prediction is endogenously generated. Third, the conflation of averages with margins. We illustrate these points with two predictors that are commonly misused: readmissions and mortality. We argue that on the one hand, ignoring these problems can lead to highly misleading applications; on the other hand, judicious choice of applications and methods can allow one to circumvent these problems.
Sendhil Mullainathan is the Robert C. Waggoner Professor of Economics at Harvard University. His work runs a wide gamut: the impact of poverty on mental bandwidth; whether CEO pay is excessive; using fictitious resumes to measure discrimination; showing that higher cigarette taxes make smokers happier; modeling how competition affects media bias; and a model of coarse thinking. His latest research focuses on using machine learning to better understand human behavior and health. He enjoys writing, having recently co-authored Scarcity: Why Having too Little Means so Much as well as writing regularly for the New York Times. He is currently co-writing a PhD textbook on machine learning. He also is active in application of research insights. He helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, and has worked in government in various roles. He is a recipient of the MacArthur “genius” Award, has been designated a “Young Global Leader” by the World Economic Forum, labeled a “Top 100 Thinker” by Foreign Policy Magazine, and named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK).