5 papers
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies
Christina X. Ji, Michael Oberst, Sanjat Kanjilal +1
Reinforcement learning (RL) has the potential to significantly improve clinical decision making. However, treatment policies learned via RL from observational data are sensitive to…
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes
Soorajnath Boominathan, Michael Oberst, Helen Zhou +2
In several medical decision-making problems, such as antibiotic prescription, laboratory testing can provide precise indications for how a patient will respond to different treatme…
ML4H Abstract Track 2019
Matthew B. A. McDermott, Emily Alsentzer, Sam Finlayson +5
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019. This index is not complete, as some accepted abstracts chose to opt-out…
Characterization of Overlap in Observational Studies
Michael Oberst, Fredrik D. Johansson, Dennis Wei +4
Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate th…
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
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 ou…