Oberst, Michael

12 publications

TMLR 2025 Expert Routing with Synthetic Data for Domain Incremental Learning Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary Chase Lipton

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

AISTATS 2024 Auditing Fairness Under Unobserved Confounding Yewon Byun, Dylan Sam, Michael Oberst, Zachary 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 A Philippakis, David Sontag

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

NeurIPS 2022 Evaluating Robustness to Dataset Shift via Parametric Robustness Sets Nikolaj Thams, Michael Oberst, David Sontag

ICMLW 2022 Evaluating Robustness to Dataset Shift via Parametric Robustness Sets Michael Oberst, Nikolaj Thams, David Sontag

NeurIPS 2022 Falsification Before Extrapolation in Causal Effect Estimation Zeshan M Hussain, Michael Oberst, Ming-Chieh Shih, David Sontag

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

ICML 2021 Regularizing Towards Causal Invariance: Linear Models with Proxies Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag

AISTATS 2020 Characterization of Overlap in Observational Studies Michael Oberst, Fredrik Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, Kush Varshney

ICML 2019 Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models Michael Oberst, David Sontag