Michael Oberst
12 papers · 2019–2026 · 5 venues · across top CS/AI venues
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Achievements
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Deep Specialist
(10)
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(58)
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Research topics
Artificial Intelligence
(11)
Core AI
(11)
Machine Learning
(10)
Learning Types
(6)
Optimization & Theory
(6)
Application Areas
(5)
Healthcare & Medicine
(3)
Clinical
(3)
Core Methods
(2)
Deep Learning
(1)
Knowledge & Reasoning
(1)
Natural Language Processing
(1)
Learning Paradigms
(1)
Models
(1)
Reasoning
(1)
Keywords
causal inference
(8)
randomized controlled trial
(4)
observational study
(3)
off-policy evaluation
(2)
doubly robust estimator
(2)
structural causal model
(2)
decision making
(1)
importance sampling
(1)
domain adaptation
(1)
causal effect estimation
(1)
model evaluation
(1)
partially observable markov decision process
(1)
distribution shift
(1)
worst-case optimization
(1)
algorithmic fairness
(1)
dataset shift
(1)
sequential decision making
(1)
risk estimation
(1)
parametric model
(1)
minimum volume sets
(1)
Papers
Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness
UAI 2026
Just Trial Once: Ongoing Causal Validation of Machine Learning Models
UAI 2025
Auditing Fairness under Unobserved Confounding
AISTATS 2024
Benchmarking Observational Studies with Experimental Data under Right-Censoring
AISTATS 2024
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?
EMNLP 2024
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions
AISTATS 2023
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
NeurIPS 2022
Falsification before Extrapolation in Causal Effect Estimation
NeurIPS 2022
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
NeurIPS 2021
Regularizing towards Causal Invariance: Linear Models with Proxies
ICML 2021
Characterization of Overlap in Observational Studies
AISTATS 2020
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
ICML 2019