Michael Oberst

About Michael Oberst

Michael Oberst, With an exceptional h-index of 6 and a recent h-index of 6 (since 2020), a distinguished researcher at Massachusetts Institute of Technology, specializes in the field of Machine Learning, Causal Inference, Healthcare.

His recent articles reflect a diverse array of research interests and contributions to the field:

Benchmarking observational studies with experimental data under right-censoring

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium

Falsification of internal and external validity in observational studies via conditional moment restrictions

Towards Rigorously Tested & Reliable Machine Learning for Health

Falsification before Extrapolation in Causal Effect Estimation

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets

Understanding the risks and rewards of combining unbiased and possibly biased estimators, with applications to causal inference

Finding regions of heterogeneity in decision-making via expected conditional covariance

Michael Oberst Information

University

Position

___

Citations(all)

291

Citations(since 2020)

291

Cited By

54

hIndex(all)

6

hIndex(since 2020)

6

i10Index(all)

6

i10Index(since 2020)

6

Email

University Profile Page

Google Scholar

Michael Oberst Skills & Research Interests

Machine Learning

Causal Inference

Healthcare

Top articles of Michael Oberst

Benchmarking observational studies with experimental data under right-censoring

2024/4/18

Falsification of internal and external validity in observational studies via conditional moment restrictions

2023/4/11

Michael Oberst
Michael Oberst

H-Index: 2

David Sontag
David Sontag

H-Index: 38

Towards Rigorously Tested & Reliable Machine Learning for Health

2023

Falsification before Extrapolation in Causal Effect Estimation

Advances in Neural Information Processing Systems

2022/12/6

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets

Advances in Neural Information Processing Systems

2022/12/6

Michael Oberst
Michael Oberst

H-Index: 2

David Sontag
David Sontag

H-Index: 38

Understanding the risks and rewards of combining unbiased and possibly biased estimators, with applications to causal inference

arXiv preprint arXiv:2205.10467

2022/5/21

Finding regions of heterogeneity in decision-making via expected conditional covariance

Advances in Neural Information Processing Systems

2021/12/6

Regularizing towards causal invariance: Linear models with proxies

2021/7/1

Trajectory inspection: A method for iterative clinician-driven design of reinforcement learning studies

AMIA Summits on Translational Science Proceedings

2021

A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection

Science translational medicine

2020/11/4

Predicting human health from biofluid-based metabolomics using machine learning

Scientific Reports

2020

Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes

2020/8/23

Machine Learning for Health (ML4H) 2019: What Makes Machine Learning in Medicine Different?

2020/4/30

Characterization of Overlap in Observational Studies

2020/6/3

See List of Professors in Michael Oberst University(Massachusetts Institute of Technology)

Co-Authors

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