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2020 – today
- 2023
[i10]Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag:
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. CoRR abs/2301.13133 (2023)- 2022
- [i9]Nikolaj Thams, Michael Oberst, David A. Sontag:
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. CoRR abs/2205.15947 (2022) - [i8]Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag:
Falsification before Extrapolation in Causal Effect Estimation. CoRR abs/2209.13708 (2022) - 2021
- [c6]Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag:
Regularizing towards Causal Invariance: Linear Models with Proxies. ICML 2021: 8260-8270 - [c5]Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag:
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. NeurIPS 2021: 15328-15343 - [i7]Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag:
Regularizing towards Causal Invariance: Linear Models with Proxies. CoRR abs/2103.02477 (2021) - [i6]Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag:
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. CoRR abs/2110.14508 (2021) - 2020
- [c4]Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. AISTATS 2020: 788-798 - [c3]Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag:
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. KDD 2020: 1937-1947 - [i5]Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca:
ML4H Abstract Track 2019. CoRR abs/2002.01584 (2020) - [i4]Sooraj Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal
, David A. Sontag:
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. CoRR abs/2006.00927 (2020) - [i3]Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David A. Sontag:
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies. CoRR abs/2010.04279 (2020)
2010 – 2019
- 2019
- [c2]Michael Oberst, David A. Sontag:
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. ICML 2019: 4881-4890 - [c1]Adrian V. Dalca, Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Corey Chivers, Andrew Beam, Tristan Naumann, Brett K. Beaulieu-Jones:
Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? ML4H@NeurIPS 2019: 1-9 - [e1]Adrian V. Dalca, Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Brett K. Beaulieu-Jones:
Machine Learning for Health Workshop, ML4H@NeurIPS 2019, Vancouver, BC, Canada, 13 December 2019. Proceedings of Machine Learning Research 116, PMLR 2019 [contents] - [i2]Michael Oberst, David A. Sontag:
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. CoRR abs/1905.05824 (2019) - [i1]Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. CoRR abs/1907.04138 (2019)
Coauthor Index
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