Papers

See my Google Scholar Page for an up-to-date list of publications. Current group members are in bold.

Preprints

How to post-train on a surrogate: Envelope sampling mitigates reward hacking
Sanjit Dandapanthula, Shuvom Sadhuka, Samir Khan, Michael Oberst, Aaditya Ramdas, Alexandra Chouldechova
preprint

Evaluating Rubric Generation with Interventional Transfer
Erik Skalnes, Layne C. Price, Raviteja Anantha, Michael Oberst
preprintcode

Understanding the Risks and Rewards of Combining Unbiased and Possibly Biased Estimators, with Applications to Causal Inference
Michael Oberst, Alexander D’Amour, Minmin Chen, Yuyan Wang, David Sontag, Steve Yadlowsky
preprint

Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling
Benjamin D. Killeen, Bohua Wan, Aditya V. Kulkarni, Nathan Drenkow, Michael Oberst, Paul H. Yi, Mathias Unberath
preprint

Conference Papers

Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering
Jiashuo Zhang, Yuling Chen, Yvonne Commodore-Mensah, Michael Oberst
Machine Learning for Health Symposium, Proceedings Track (ML4H), 2026
paper

Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation
Daniel P. Jeong, Charles Q. Li, Hossein Hosseiny, Nitya M. Bhalla, Fatma Uyar Morency, Pradeep Ravikumar, Zachary C. Lipton, Michael Oberst
Neural Information Processing Systems, Evaluation and Datasets Track (NeurIPS), 2026
paper

Fixed Size Active Statistical Inference
Erik Skalnes, Michael Oberst
Neural Information Processing Systems (NeurIPS), 2026
paper

Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness
Jonathan Zhang, Erik Skalnes, Jacob Chen, Michael Oberst
Conference on Uncertainty in Artificial Intelligence (UAI), 2026
paper

No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference
Pranav Mani, Peng Xu, Zachary C. Lipton, Michael Oberst
International Conference on Machine Learning (ICML), 2026
paper

Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context
Andrew Wang, Jiashuo Zhang, Michael Oberst
Conference on Health, Inference, and Learning (CHIL), 2026
paper

Just Trial Once: Ongoing Causal Validation of Machine Learning Models
Jacob M. Chen, Michael Oberst
Conference on Uncertainty in Artificial Intelligence (UAI), 2025
Oral Presentation (3% of submissions, 9% of accepted papers)
paperposter

Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?
Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024
paperextended version

Auditing Fairness under Unobserved Confounding
Emily Byun, Dylan Sam, Michael Oberst, Zachary Lipton, Bryan Wilder
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
paper

Benchmarking Observational Studies with Experimental Data under Right-Censoring
Ilker Demirel, Edward De Brouwer, Zeshan Hussain, Michael Oberst, Anthony Philippakis, David Sontag
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
paper

Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions
Zeshan Hussain*, Ming-Chieh Shih*, Michael Oberst, Ilker Demirel, David Sontag
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
paper *Equal Contribution

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
Nikolaj Thams*, Michael Oberst*, David Sontag
Neural Information Processing Systems (NeurIPS), 2022
papercode *Equal Contribution, order determined by coin flip

Falsification before Extrapolation in Causal Effect Estimation
Zeshan Hussain*, Michael Oberst*, Ming-Chieh Shih*, David Sontag
Neural Information Processing Systems (NeurIPS), 2022
paper *Equal Contribution, alphabetical order

Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
Justin Lim*, Christina X. Ji*, Michael Oberst*, Saul Blecker, Leora Horwitz, David Sontag
Neural Information Processing Systems (NeurIPS), 2021
papervideocode *Equal Contribution

Regularizing towards Causal Invariance: Linear Models with Proxies
Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag
International Conference on Machine Learning (ICML), 2021
papervideoslidespostercode

Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies
Christina X. Ji*, Michael Oberst*, Sanjat Kanjilal, David Sontag
AMIA Virtual Informatics Summit, 2021
papervideocode *Equal Contribution

Characterization of Overlap in Observational Studies
Michael Oberst*, Fredrik D. Johansson*, Dennis Wei*, Tian Gao, Gabriel Brat, David Sontag, Kush R. Varshney
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
papervideocode *Equal Contribution

Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes
Sooraj Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David Sontag
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
papervideocodedataset

Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
Michael Oberst, David Sontag
International Conference on Machine Learning (ICML), 2019
paperslidespostervideo

Journal Papers

Clinical trials for continuously monitored and updated AI systems
Wouter A. C. van Amsterdam, Michael Oberst, Jean Feng, Jenna Wiens, Shengpu Tang, Shalmali Joshi, Rajesh Ranganath, Mark Sendak, Uri Shalit, Julia E. Vogt, Brett Beaulieu-Jones, Muhammad Mamdani, David Kent, Patrick J. Heagerty, Thomas R. Fleming, Anna Goldenberg
Nature Medicine, 2026
article

A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice
Majid Afshar, Felice Resnik, Mary Ryan Baumann, Josie Hintzke, Kayla Lemmon, Anne Gravel Sullivan, Tina Shah, Anthony Stordalen, Michael Oberst, Jason Dambach, Leigh Ann Mrotek, Mariah Quinn, Kirsten Abramson, Peter Kleinschmidt, Tom Brazelton, Heidi Twedt, David Kunstman, Graham Wills, John Long, Brian W. Patterson, Frank J. Liao, Stacy Rasmussen, Elizabeth Burnside, Cherodeep Goswami, Joel E. Gordon
NEJM AI, 2025
article

Expert Routing with Synthetic Data for Domain Incremental Learning
Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary C. Lipton
Transactions on Machine Learning Research (TMLR), 2025
paper

A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection
Sanjat Kanjilal, Michael Oberst, Sooraj Boominathan, Helen Zhou, David C. Hooper, David Sontag
Science Translational Medicine, 2020
articlecodedataset

Predicting Human Health from Biofluid-Based Metabolomics using Machine Learning
Ethan D. Evans, Claire Duvallet, Nathaniel D. Chu, Michael Oberst, Michael A. Murphy, Isaac Rockafellow, David Sontag, Eric J. Alm
Scientific Reports, 2020
article

Other Publications

Towards Rigorously Tested and Reliable Machine Learning for Health
Michael Oberst
PhD Dissertation in EECS, MIT
full text

Counterfactual Policy Introspection using Structural Causal Models
Michael Oberst
M.S. Thesis in EECS, MIT
full textnote on errata