Hello! I am an applied microeconomist working at the intersection of health and behavioral economics. My research investigates how individuals form beliefs and make decisions in important health contexts like diagnostic medicine, mental illness, and public health crises. I study these topics using algorithmic tools and randomized control trials.
My job market paper, X-raying Experts, combines machine learning with an econometric framework to analyze human errors in radiology, a crucial step in the diagnostic pipeline.
Before graduate school, I received an AB in Statistics from Harvard University and worked as a data scientist at Civis Analytics, where I consulted for political campaigns, nonprofits, and tech companies.
Job Market Paper
X-raying Experts: Decomposing Systematic Mistakes in Radiology
Abstract
Medical errors are consequential but difficult to study without laborious human review of past cases. I apply algorithmic tools to measure the extent and nature of medical error in one of the most common medical decision settings: chest x-ray interpretation. Using anonymized medical records from a large hospital, I compare radiologists' claims about cardiac health to algorithmic predictions of the same, adjudicating between the two using exogenously administered blood tests. At least 55 percent of radiologists make mistakes, issuing reports that predictably misrank the severity of patients' cardiac health. Careful choice of algorithmic benchmark shows that these errors reflect, in roughly equal proportion, individual radiologists falling short of best clinical practice (a "human frontier"), and a further gap between best practice and algorithmic predictions (a "machine frontier"). Reaching the human frontier would reduce radiologists' false negative rates by 20% and false positive rates by 2%; reaching the machine frontier would reduce false negatives by an additional 12% and false positives by 2%. In contrast to a leading hypothesis in the medical literature, errors do not reflect radiologists overweighting salient information; rather, they systematically under-react to signals of patient risk. Finally, the mistakes revealed by machine learning do not skew against underrepresented groups.Publications
When Guidance Changes: Government Stances and Public Beliefs, with Charlie Rafkin and Pierre-Luc Vautrey. Journal of Public Economics (2021).
[ Preprint ]
[ Survey Instrument ]
[ Replication Materials ]
Working Papers
Managing Emotions: The Effects of Online Mindfulness Meditation on Mental Health and Economic Behavior, with Pierre-Luc Vautrey. In Progress.
Press: The Economist.
Works in Progress
Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations, with Manuel Quintero, Will Stephenson, and Tamara Broderick.
Draft Coming Soon