Papers

See my Google Scholar Page for an up-to-date list of publications.

Journal Papers

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
[article], [code], [dataset]

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]

Conference Papers

Robustly Quantifying Inequity in Resource Allocation
Emily Byun, Dylan Sam, Michael Oberst, Zachary Lipton, Bryan Wilder
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024

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

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
[paper], [code] *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
[paper], [video], [code] *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
[paper], [video], [slides], [poster], [code]

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
[paper], [video], [code] *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
[paper], [video], [code] *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
[paper], [video], [code], [dataset]

Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
Michael Oberst, David Sontag
International Conference on Machine Learning (ICML), 2019
[paper], [slides], [poster], [video]

Preprints

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
[paper]

Other Publications

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