SMU SOE Seminar (December 2, 2022): Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits

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ABSTRACT We develop asymptotic approximation results that can be applied to sequential estimation and inference problems, adaptive randomized controlled trials, and other statistical decision problems that can be cast as involving multiple decision nodes with structured and possibly endogenous information sets. Our results extend the classic Asymptotic Representation Theorem that provides a basis for efficiency bound theory and local power analysis. In adaptive settings where the decision at one stage can affect the observation of variables in later stages, we develop a limiting Gaussian bandit representation that characterizes all attainable limit distributions under local alternatives. We illustrate how the theory can be applied to study the choice of adaptive rules and end-of-sample statistical inference in batched (groupwise) sequential adaptive experiments. Click here to view the CV.
		PRESENTER Keisuke Hirano The Pennsylvania State University
		RESEARCH FIELDS Econometrics
		DATE: 2 December 2022 (Friday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903