SMU SOE Seminar (August 4, 2023): How to Sample and When to Stop Sampling: The Generalized Wald Problem and Minimax Policies

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TOPIC:

HOW TO SAMPLE AND WHEN TO STOP SAMPLING: THE GENERALIZED WALD PROBLEM AND MINIMAX POLICIES

ABSTRACT Acquiring information is expensive. Experimenters need to carefully choose how many units of each treatment to sample and when to stop sampling. The aim of this paper is to develop techniques for incorporating the cost of information into experimental design. In particular, we study sequential experiments where sampling is costly and a decision-maker aims to determine the best treatment for full scale implementation by (1) adaptively allocating units to two possible treatments, and (2) stopping the experiment when the expected welfare (inclusive of sampling costs) from implementing the chosen treatment is maximized. Working under the diffusion limit, we describe the optimal policies under the minimax regret criterion. Under small cost asymptotics, the same policies are also optimal under parametric and non-parametric distributions of outcomes. The minimax optimal sampling rule is just the Neyman allocation; itis independent of sampling costs and does not adapt to previous outcomes. The decision-maker stops sampling when the average difference between the treatment outcomes, multiplied by the number of observations collected until that point, exceeds a specific threshold. We also suggest methods for inference on the treatment effects using stopping times and discuss their optimality. Click here to view the CV.
		PRESENTER Karun Adusumilli University of Pennsylvania
		RESEARCH FIELDS Econometrics
		DATE: 4 August 2023 (Friday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903

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