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Activity Number:
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295
- Causal, Robust, and Machine Learning for Survival Outcomes
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Type:
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Invited
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Date/Time:
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Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Lifetime Data Science Section
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Abstract #317082
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Title:
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Some Advances in Finite-Horizon Reinforcement Learning for Time-to-Event Outcomes
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Author(s):
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Michael Kosorok*
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Companies:
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University of North Carolina at Chapel Hill
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Keywords:
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Abstract:
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In the presentation, we describe a new, nonparametric off-policy, finite-horizon reinforcement learning methodology for right censored data. The method is flexible enough to allow for censoring which is conditionally independent given the available history as well as for visit times that may be intrinsically dependent on treatment response. We show that the proposed method is asymptotically consistent and has some demonstrable advantages over alternative approaches in both simulations and in the analysis of data from a hybrid randomized and observational study of leukemia treatment.
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Authors who are presenting talks have a * after their name.
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