Backward Induction

Friday, November 9, 2018 - 10:40am - 11:10am
Min Zhang (University of Michigan)
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual’s own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes.
Wednesday, November 7, 2018 - 10:10am - 10:40am
Thomas Murray (University of Minnesota, Twin Cities)
This talk will describe a new approach for optimizing dynamic treatment regimes that bridges the gap between Bayesian inference and Q-learning. The proposed approach fits a series of Bayesian regression models, one for each stage, in reverse sequential order. Each model regresses the remaining payoff assuming optimal actions are taken at subsequent stages on the current history and actions.
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