no code implementations • 30 Jan 2023 • Zeshan Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David Sontag
Our approach is interpretable, allowing a practitioner to visualize which subgroups in the population lead to falsification of an observational study.
1 code implementation • 27 Sep 2022 • Zeshan Hussain, Michael Oberst, Ming-Chieh Shih, David Sontag
Under the assumption that at least one observational estimator is asymptotically normal and consistent for both the validation and extrapolated effects, we provide guarantees on the coverage probability of the intervals output by our algorithm.
1 code implementation • 31 May 2022 • Nikolaj Thams, Michael Oberst, David Sontag
We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance.
1 code implementation • NeurIPS 2021 • Justin Lim, Christina X Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David Sontag
Individuals often make different decisions when faced with the same context, due to personal preferences and background.
1 code implementation • 3 Mar 2021 • Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag
In the case of two proxy variables, we propose a modified estimator that is prediction optimal under interventions up to a known strength.
1 code implementation • 8 Oct 2020 • Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David Sontag
Reinforcement learning (RL) has the potential to significantly improve clinical decision making.
1 code implementation • 1 Jun 2020 • Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David Sontag
In several medical decision-making problems, such as antibiotic prescription, laboratory testing can provide precise indications for how a patient will respond to different treatment options.
no code implementations • 5 Feb 2020 • Matthew B. A. McDermott, Emily Alsentzer, Sam Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019.
1 code implementation • 9 Jul 2019 • Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, Kush R. Varshney
Overlap between treatment groups is required for non-parametric estimation of causal effects.
1 code implementation • 14 May 2019 • Michael Oberst, David Sontag
We introduce an off-policy evaluation procedure for highlighting episodes where applying a reinforcement learned (RL) policy is likely to have produced a substantially different outcome than the observed policy.