Michael Oberst

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 23

Top Topics

Internal And External

Causal Relations

Top Venues

Publications

Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst, Anthony Philippakis, David A. Sontag
Benchmarking Observational Studies with Experimental Data under Right-Censoring. AISTATS (2024)
Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder
Auditing Fairness under Unobserved Confounding. AISTATS (2024)
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapa, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason A. Fries, Parisa Rashidi, Brett K. Beaulieu-Jones, Xuhai "Orson" Xu, Matthew B. A. McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gürsoy, Marzyeh Ghassemi, Emma Pierson, George H. Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. CoRR (2024)
Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder
Auditing Fairness under Unobserved Confounding. CoRR (2024)
Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. CoRR (2023)
Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. AISTATS (2023)
Nikolaj Thams, Michael Oberst, David A. Sontag
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. CoRR (2022)
Nikolaj Thams, Michael Oberst, David A. Sontag
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. NeurIPS (2022)
Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag
Falsification before Extrapolation in Causal Effect Estimation. CoRR (2022)
Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag
Falsification before Extrapolation in Causal Effect Estimation. NeurIPS (2022)
Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. CoRR (2021)
Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. NeurIPS (2021)
Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
Regularizing towards Causal Invariance: Linear Models with Proxies. ICML (2021)
Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
Regularizing towards Causal Invariance: Linear Models with Proxies. CoRR (2021)
Sooraj Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. CoRR (2020)
Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. KDD (2020)
Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David A. Sontag
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies. CoRR (2020)
Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David A. Sontag, Kush R. Varshney
Characterization of Overlap in Observational Studies. AISTATS (2020)
Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca
ML4H Abstract Track 2019. CoRR (2020)
Michael Oberst, David A. Sontag
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. ICML (2019)
Michael Oberst, David A. Sontag
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. CoRR (2019)
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
Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? ML4H@NeurIPS (2019)
Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel Brat, David A. Sontag, Kush R. Varshney
Characterization of Overlap in Observational Studies. CoRR (2019)