Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. - Michael Oberst; David A. Sontag (ICML, 2019)

Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? - Adrian V. Dalca; Matthew B. A. McDermott; Emily Alsentzer; Samuel G. Finlayson; Michael Oberst; Fabian Falck; Corey Chivers; Andrew Beam; Tristan Naumann; Brett K. Beaulieu-Jones (ML4H@NeurIPS, 2019)

Characterization of Overlap in Observational Studies. - Michael Oberst; Fredrik D. Johansson; Dennis Wei; Tian Gao; Gabriel A. Brat; David A. Sontag; Kush R. Varshney (AISTATS, 2020)

Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. - Soorajnath Boominathan; Michael Oberst; Helen Zhou; Sanjat Kanjilal; David A. Sontag (KDD, 2020)

ML4H Abstract Track 2019. - Matthew B. A. McDermott; Emily Alsentzer; Samuel G. Finlayson; Michael Oberst; Fabian Falck; Tristan Naumann; Brett K. Beaulieu-Jones; Adrian V. Dalca (CoRR, 2020)

Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies. - Christina X. Ji; Michael Oberst; Sanjat Kanjilal; David A. Sontag (CoRR, 2020)

Regularizing towards Causal Invariance: Linear Models with Proxies. - Michael Oberst; Nikolaj Thams; Jonas Peters; David A. Sontag (ICML, 2021)

Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. - Justin Lim; Christina X. Ji; Michael Oberst; Saul Blecker; Leora Horwitz; David A. Sontag (NeurIPS, 2021)

Falsification before Extrapolation in Causal Effect Estimation. - Zeshan M. Hussain; Michael Oberst; Ming-Chieh Shih; David A. Sontag (NeurIPS, 2022)

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. - Nikolaj Thams; Michael Oberst; David A. Sontag (NeurIPS, 2022)

Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. - Zeshan M. Hussain; Ming-Chieh Shih; Michael Oberst; Ilker Demirel; David A. Sontag (AISTATS, 2023)

Towards Rigorously Tested & Reliable Machine Learning for Health - Michael Oberst (2023)

Auditing Fairness under Unobserved Confounding. - Yewon Byun; Dylan Sam; Michael Oberst; Zachary C. Lipton; Bryan Wilder (AISTATS, 2024)

Benchmarking Observational Studies with Experimental Data under Right-Censoring. - Ilker Demirel; Edward De Brouwer; Zeshan M. Hussain; Michael Oberst; Anthony Philippakis; David A. Sontag (AISTATS, 2024)

Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? - Daniel P. Jeong; Saurabh Garg; Zachary C. Lipton; Michael Oberst (EMNLP, 2024)

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. - Hyewon Jeong; Sarah Jabbour; Yuzhe Yang 003; Rahul Thapa; Hussein Mozannar; William Jongwon Han; Nikita Mehandru; Michael Wornow; Vladislav Lialin; Xin Liu 034; Alejandro Lozano; Jiacheng Zhu; et al. (CoRR, 2024)

The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models. - Daniel P. Jeong; Pranav Mani; Saurabh Garg; Zachary C. Lipton; Michael Oberst (CoRR, 2024)

Generate to Discriminate: Expert Routing for Continual Learning. - Yewon Byun; Sanket Vaibhav Mehta; Saurabh Garg; Emma Strubell; Michael Oberst; Bryan Wilder; Zachary C. Lipton (CoRR, 2024)

Just Trial Once: Ongoing Causal Validation of Machine Learning Models. - Jacob M. Chen; Michael Oberst (UAI, 2025)

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 (CoRR, 2025)

No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference. - Pranav Mani; Peng Xu; Zachary C. Lipton; Michael Oberst (CoRR, 2025)