Working papers
I’m working on it.
Selected publications
For a more complete list, please see my Google Scholar or PubMed pages.
Health economics + Machine learning
- The Health Costs of Cost-Sharing, QJE 2024 (with Amitabh Chandra and Evan Flack). Editor’s Choice, QJE. Media: Vox, Modern Healthcare. Awards: Finalist, NIHCM Foundation Research Award.
- Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care, QJE 2022 (with Sendhil Mullainathan). Editor’s Choice, QJE. Git repo here. Slide deck here. Summary and comment (with Kate Baicker) in JAMA Health Forum here. Awards: International Health Economics Association Arrow Award 2023.
- Predictive modeling of US healthcare spending in late life, Science 2018 (with Liran Einav, Amy Finkelstein, and Sendhil Mullainathan)
- Does Machine Learning Automate Moral Hazard and Error? AER: P&P 2017 (with Sendhil Mullainathan)
- Early death after discharge from emergency departments, BMJ 2017(with Brent Cohn, Michael Wilson, Bapu Jena, and David Cutler)
- Prediction Policy Problems, AER: P&P 2015 (with Jon Kleinberg, Jens Ludwig, and Sendhil Mullainathan)
Medicine + Machine learning
- Pooled testing efficiency increases with test frequency, PNAS 2022 (with Ned Augenblick, Jon Kolstad, and Ao Wang). Media: MIT Tech Review
- Variation in common laboratory test results caused by ambient temperature, Med (Cell Press) 2021 (with Devin Pope)
- An algorithmic approach to unexplained pain disparities in underserved populations, Nature Medicine 2021 (with Emma Pierson, David Cutler, Jure Leskovec, and Sendhil Mullainathan). Git repo here. Slide deck here. Media: Wired, MIT Tech Review, STAT, NPR’s Science Friday featured segment, FT Tech Tonic
- Direct Uncertainty Prediction for Medical Second Opinions, ICML 2019 (with Maithra Raghu, Katy Blumer, Rory Sayres, Robert Kleinberg, Sendhil Mullainathan, and Jon Kleinberg)
- Regularizing Latent Variable Models with Discriminative Models, ICML 2019 (with Andy Miller, Jon Cunningham, and Sendhil Mullainathan)
- Individuals’ body temperatures vary meaningfully and predict mortality, BMJ 2017 (with Jasmeet Samra and Sendhil Mullainathan)
Algorithmic bias
- Race adjustments in clinical algorithms can help correct for racial disparities in data quality. PNAS 2024 (with Anna Zink and Emma Pierson). Invited PNAS commentary by John Mullahy: Clinical decisions, patient race, and flawed data.
- A ‘Playbook‘ for identifying and fixing algorithmic bias. Media: STAT News. Regulatory: FTC PrivacyCon 2021, ONC Annual Meeting, NCQA Digital Quality Summit, CA Dept. of Fair Employment & Housing hearing
- On the Inequity of Predicting A While Hoping for B. AER: P&P 2021 (with Sendhil Mullainathan)
- Racial bias in allocation of COVID-19 relief funding, JAMA 2020 (with Pragya Kakani, Amitabh Chandra, and Sendhil Mullainathan). Git repo here. Media: Wall Street Journal, STAT
- Dissecting racial bias in an algorithm used to manage the health of populations, Science 2019 (with Brian Powers, Christine Vogeli, and Sendhil Mullainathan). Git repo here. Media: Wall Street Journal, Washington Post, LA Times, AP, Guardian, Telegraph, Bloomberg, Wired, Scientific American, STAT, Nature, Breitbart (!), and many others. Awards: Altmetric 2019 Top 100, ‘Editors’ Pick’: STAT Madness 2020, Willard G. Manning Memorial Award for the Best Research in Health Econometrics 2021, Financial Times Responsible Business Education Award. Regulatory action: The study prompted the NY state insurance regulator and Sens. Booker and Wyden to initiate inquiries into algorithmic bias (in WSJ, Wired).