Highlighted Publications




 

Shekhar, Leder-Luis, and Akoglu (2026) ask whether unsupervised, explainable machine learning can effectively identify hospitals engaging in potentially fraudulent Medicare billing. They analyze millions of Medicare inpatient claims from 2017, combined with patients’ prior medical histories and hospital characteristics, covering over 2,200 hospitals. Using anomaly-detection algorithms, they rank hospitals based on suspicious coding and spending patterns. The authors find that

Darolia and Turner (2026) ask whether the rapid expansion of apprenticeships in the United States is being matched by credible evidence on program quality, outcomes, and skill development. They examine administrative data from the U.S. Department of Labor’s Registered Apprenticeship system, state-level apprenticeship records, and postsecondary enrollment and completion data. They find that apprenticeships have grown substantially since 2011, expanded into new industries, and

Cowell et al. (2025) ask whether there are specific weeks during pregnancy when exposure to fine particulate air pollution (PM2.5) has the strongest association with birth weight. They analyze data from 16,868 full-term singleton births in the U.S. Environmental Influences on Child Health Outcomes (ECHO) cohort, using weekly, address-level PM2.5 exposure estimates derived from machine-learning models. They find that higher prenatal PM2.5 exposure is associated with lower birt

Christakis et al. (2026) examine whether adult attitudes—especially parental attitudes—support banning student smartphone access during the school day. They ask whether support for school smartphone bans is widespread across countries and which individual characteristics predict that support. They analyze cross-sectional survey data from 35,018 adults across 35 countries, using logistic regression to adjust for demographics, parental status, life satisfaction, and digital beh