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SLDS March Webinar -- 1 PM Eastern Time March 26 -- Lihua Lei -- Stanford

  • 1.  SLDS March Webinar -- 1 PM Eastern Time March 26 -- Lihua Lei -- Stanford

    Posted 2 hours ago

    Dear Friends,

    This is a reminder that our SLDS March webinar will be held on Mar 26, 1 pm Eastern Time, featured by Dr. Lihua Lei from Stanford. Hope to see you there!  

    Title:                       Compound Selection Decisions: An Almost SURE Approach

    Speakers:              Dr. Lihua Lei, Stanford University

    Date and Time:      Mar 26, 2026, 1:00 to 2:30 pm Eastern Time

    Registration Link:  ASA SLDS Webinar Registration Link [eventbrite.com] 
     
    Abstract:                This talk proposes methods for producing compound selection decisions in a Gaussian sequence model. Given unknown, fixed parameters μ1:n and known σ1:n with observations Yi∼𝖭(μi,σi2), the decision maker would like to select a subset of indices S so as to maximize utility ∑i∈S(μi−Ki)/n, for known costs Ki. Inspired by Stein's unbiased risk estimate (SURE), we introduce an almost unbiased estimator, called ASSURE, for the expected utility of a proposed decision rule. ASSURE allows a user to choose a welfare-maximizing rule from a pre-specified class by optimizing the estimated welfare, thereby producing selection decisions that borrow strength across noisy estimates. We show that ASSURE produces decision rules that are asymptotically no worse than the optimal but infeasible decision rule in the pre-specified class. We apply ASSURE to the selection of Census tracts for economic opportunity, the identification of discriminating firms, and the analysis of p-value decision procedures in A/B testing.

    Presenter:               Lihua Lei is an assistant professor at Stanford Graduate School of Business and an Assistant Professor of Statistics (by courtesy). He got his PhD in statistics at UC Berkeley and spent three years at Stanford Statistics as a postdoc. His research areas include causal inference, ML/AI-assisted inference, econometrics, experimental design, compound decision theory, multiple testing, network clustering, and stochastic optimization.



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    Boxiang Wang
    Associate Professor
    Department of Statistics and Actuarial Science
    University of Iowa
    Iowa City, IA, United States
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