I’m a CS PhD student at MIT, working with David Sontag in the Clinical Machine Learning research group. My research spans causality and machine learning, with a few broad themes:

  • Learning how to act from retrospective data: I am motivated by learning policies from retrospective data in high-risk domains (such as healthcare) where direct experimentation may not be feasible. This has motivated my work on learning policies under domain-specific constraints, such as making trade-offs between multiple objectives, or incorporating deferral to experts [KDD 2020].
  • “Debugging” causal models: However, learning policies from retrospective data requires causal assumptions, and the available data can limit what types of policies can be reliably evaluated on certain subpopulations. This has motivated my work on developing methods to help domain experts introspect causal models using counterfactuals [ICML 2019, MS Thesis], and get interpretable characterization of subpopulations where a given policy can be evaluated [AISTATS 2020].

Conference Papers

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], [code]

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]

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]

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


AMR-UTI: Antimicrobial Resistance in Urinary Tract Infections
Michael Oberst, Sooraj Boominathan, Helen Zhou, Sanjat Kanjilal, David Sontag
[Homepage], [PhysioNet Page]


Primer: Learning Treatment Policies from Observational Data
Models, Inference, and Algorithms Seminar
Broad Institute, September 23rd, 2020

Other Publications

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