Michael Oberst

Orcid: 0000-0003-1720-6702

According to our database1, Michael Oberst authored at least 11 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Falsification before Extrapolation in Causal Effect Estimation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Regularizing towards Causal Invariance: Linear Models with Proxies.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies.
CoRR, 2020

ML4H Abstract Track 2019.
CoRR, 2020

Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Characterization of Overlap in Observational Studies.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different?
Proceedings of the Machine Learning for Health Workshop, 2019

Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models.
Proceedings of the 36th International Conference on Machine Learning, 2019


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