Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits

Abstract: We develop asymptotic approximations that can be applied to sequential estimation and inference problems, adaptive randomized controlled trials, and related settings. In batched adaptive settings where the decision at one stage can affect the observation of variables in later stages, our asymptotic representation characterizes all limit distributions attainable through a joint choice of an adaptive design rule and statistics applied to the adaptively generated data. This facilitates local power analysis of tests, comparison of adaptive treatments rules, and other analyses of batchwise sequential statistical decision rules.