I am on the academic job market!

I’m a final-year PhD Candidate in Computer Science at MIT, advised by David Sontag.

My research lies at the intersection of causality and machine learning, with the goal of enabling reliable decision-making and prediction in high-risk domains like healthcare. During my PhD I have focused on two complementary themes:

  • Reliable causal inference and policy evaluation: Retrospective healthcare data is often used to learn better policies for treating disease, when experimentation is infeasible. However, this requires strong causal assumptions, and not all policies can be reliably evaluated. This has motivated my work on developing methods to help domain experts assess the plausibility of causal models [ICML 2019, MS Thesis], and get interpretable characterization of subpopulations where a given policy can be evaluated [AISTATS 2020]. I have also developed methods to incorporate limited clinical trial data [NeurIPS 2022, Preprint] to improve credibility.
  • Robust, reliable prediction via (partial) causal knowledge: Causality is a useful lens for reasoning about how plausible changes in distribution will impact future model performance. In linear settings, I have developed methods for learning predictors with provably robust performance across changes in factors that are not directly observed (e.g., differences in socioeconomic status of patients) [ICML 2021]. In more general settings, I have developed new ways for domain experts to express their causal intuition about plausible changes (e.g., changes in clinical practice), evaluate the worst-case performance of models under those changes, and discover changes that result in poor performance [NeurIPS 2022].

These methodological problems are informed by my applied work with clinical collaborators, such as learning antibiotic treatment policies [Science Trans. Med. 2020] and debugging reinforcement-learning models for sepsis management [AMIA 2021].

Selected publications (Full List)

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

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]

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]

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]

Preprint / Working Paper

Bias-robust Integration of Observational and Experimental Estimators
Michael Oberst, Alexander D’Amour, Minmin Chen, Yuyan Wang, David Sontag, Steve Yadlowsky
Oral presentation at the American Causal Inference Conference (ACIC), 2022

Selected Talks

Regularizing towards Causal Invariance: Linear Models with Proxies
Online Causal Inference Seminar
Stanford, March 29th, 2022
[video], [slides]

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


Head TA for 6.867 (Machine Learning), Fall 2021
Received Frederick C. Hennie III Award for teaching excellence.


Conferences: NeurIPS 2022 (Top Reviewer), ICML 2022 (Top 10% of reviewers), NeurIPS 2021, UAI 2021 (Top 5% of reviewers), AISTATS 2019

Journals: Journal of Causal Inference, Statistics & Computing, Bayesian Analysis.