| Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models M Oberst, D Sontag International Conference on Machine Learning (ICML) 2019, 2019 | 143 | 2019 |
| A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection S Kanjilal, M Oberst, S Boominathan, H Zhou, DC Hooper, D Sontag Science translational medicine 12 (568), eaay5067, 2020 | 54 | 2020 |
| Characterization of Overlap in Observational Studies M Oberst, FD Johansson, D Wei, T Gao, G Brat, D Sontag, KR Varshney 23rd International Conference on Artificial Intelligence and Statistics …, 2020 | 27 | 2020 |
| Regularizing towards causal invariance: Linear models with proxies M Oberst, N Thams, J Peters, D Sontag International Conference on Machine Learning, 8260-8270, 2021 | 24 | 2021 |
| Predicting human health from biofluid-based metabolomics using machine learning ED Evans, C Duvallet, ND Chu, MK Oberst, MA Murphy, I Rockafellow, ... Scientific reports 10 (1), 17635, 2020 | 20 | 2020 |
| Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes S Boominathan, M Oberst, H Zhou, S Kanjilal, D Sontag ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020 | 14 | 2020 |
| Finding regions of heterogeneity in decision-making via expected conditional covariance J Lim, CX Ji, M Oberst, S Blecker, L Horwitz, D Sontag Advances in Neural Information Processing Systems 34, 15328-15343, 2021 | 6 | 2021 |
| Evaluating Robustness to Dataset Shift via Parametric Robustness Sets N Thams, M Oberst, D Sontag Neural Information Processing Systems (NeurIPS) 2022, 2022 | 5 | 2022 |
| Falsification before Extrapolation in Causal Effect Estimation Z Hussain, M Oberst, MC Shih, D Sontag Neural Information Processing Systems (NeurIPS) 2022, 2022 | 4 | 2022 |
| Machine Learning for Health (ML4H) 2019: What Makes Machine Learning in Medicine Different? AV Dalca, MBA McDermott, E Alsentzer, SG Finlayson, M Oberst, F Falck, ... Machine Learning for Health Workshop, 1-9, 2020 | 4 | 2020 |
| AMR-UTI: Antimicrobial Resistance in Urinary Tract Infections (version 1.0.0) M Oberst, S Boominathan, H Zhou, S Kanjilal, D Sontag PhysioNet, 2020 | 4 | 2020 |
| Falsification of internal and external validity in observational studies via conditional moment restrictions Z Hussain, MC Shih, M Oberst, I Demirel, D Sontag International Conference on Artificial Intelligence and Statistics, 5869-5898, 2023 | 3 | 2023 |
| Bias-robust Integration of Observational and Experimental Estimators M Oberst, A D’Amour, M Chen, Y Wang, D Sontag, S Yadlowsky arXiv preprint arXiv:2205.10467, 2022 | 3 | 2022 |
| Trajectory inspection: A method for iterative clinician-driven design of reinforcement learning studies CX Ji, M Oberst, S Kanjilal, D Sontag AMIA Summits on Translational Science Proceedings 2021, 305, 2021 | 3 | 2021 |
| Understanding the risks and rewards of combining unbiased and possibly biased estimators, with applications to causal inference M Oberst, A D'Amour, M Chen, Y Wang, D Sontag, S Yadlowsky arXiv preprint arXiv:2205.10467, 2022 | 1 | 2022 |
| Auditing Fairness under Unobserved Confounding Y Byun, D Sam, M Oberst, ZC Lipton, B Wilder arXiv preprint arXiv:2403.14713, 2024 | | 2024 |
| Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium H Jeong, S Jabbour, Y Yang, R Thapta, H Mozannar, WJ Han, ... arXiv preprint arXiv:2403.01628, 2024 | | 2024 |
| Benchmarking Observational Studies with Experimental Data under Right-Censoring I Demirel, E De Brouwer, Z Hussain, M Oberst, A Philippakis, D Sontag arXiv preprint arXiv:2402.15137, 2024 | | 2024 |
| Towards Rigorously Tested & Reliable Machine Learning for Health MK Oberst Massachusetts Institute of Technology, 2023 | | 2023 |
| Counterfactual policy introspection using structural causal models MK Oberst Massachusetts Institute of Technology, 2019 | | 2019 |
David SontagProfessor, Massachusetts Institute of TechnologyVerified email at csail.mit.edu
