- The Health Costs of Cost-Sharing, NBER working paper (with Amitabh Chandra and Evan Flack).
- Media: Vox, Modern Healthcare
For a more complete list, please see my Google Scholar or PubMed pages.
Prediction policy problems
- Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care, QJE 2022 (with Sendhil Mullainathan). Git repo here. Slide deck summary here. Summary and comment (with Kate Baicker) in JAMA Health Forum here.
- 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)
- 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. Media: Wired, MIT Tech Review, STAT, NPR’s Science Friday featured segment, FT Tech Tonic
- A Comparison of Patient History- and EKG-based Cardiac Risk Scores, AMIA 2019 (with Andy Miller and Sendhil Mullainathan)
- 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)
- 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, 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).
Machine learning — perspectives
- Putting decisions under the microscope, Nature Medicine 2019
- Regulation of predictive analytics in medicine, Science 2019 (with Ravi Parikh and Amol Navathe)
- Lost in Thought—The limits of the human mind and the future of medicine, NEJM 2017 (with Thomas Lee)
- Predicting the Future: Big Data, Machine Learning, and the Future of Clinical Medicine, NEJM 2016 (with Zeke Emanuel)