| 2025 | UAI | Just Trial Once: Ongoing Causal Validation of Machine Learning Models. | Jacob M. Chen, Michael Oberst |
| 2024 | AISTATS | Auditing Fairness under Unobserved Confounding. | Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder |
| 2024 | AISTATS | Benchmarking Observational Studies with Experimental Data under Right-Censoring. | Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst, Anthony Philippakis, David A. Sontag |
| 2024 | EMNLP | Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? | Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst |
| 2023 | AISTATS | 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 |
| 2021 | ICML | Regularizing towards Causal Invariance: Linear Models with Proxies. | Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag |
| 2020 | AISTATS | Characterization of Overlap in Observational Studies. | Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney |
| 2020 | KDD | Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. | Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag |
| 2019 | ICML | Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. | Michael Oberst, David A. Sontag |