2019/09/15 by Charles F. Manski, Manski, Charles F. · 1 citation
Computer Science · Economics, Econometrics and Finance · Mathematics · #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian inference #Computer science #Decision rule #Decision theory #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Fiducial inference #Frequentist inference #Inference #Machine learning #Mathematical optimization #Mathematics #Methodology (stat.ME) #Minimax #Point (geometry) #Point estimation #Regret #Sample (material) #Statistical Methods and Inference #Statistical Methods in Clinical Trials #Statistical hypothesis testing #Statistical inference #Statistical theory #Statistics #econ.EM #stat.ME
paper · pdf · doi:10.48550/arxiv.1909.06853
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/09/15 · openalex publication_date 2019/09/15 · arxiv updated 2019/09/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Wald development of statistical decision theory addresses decision making with sample data. Wald's concept of a statistical decision function (SDF) embraces all mappings of the form [data -> decision]. An SDF need not perform statistical inference; that is, it need not use data to draw conclusions about the true state of nature. Inference-based SDFs have the sequential form [data -> inference -> decision]. This paper motivates inference-based SDFs as practical procedures for decision making that may accomplish some of what Wald envisioned. The paper first addresses binary choice problems, where all SDFs may be viewed as hypothesis tests. It next considers as-if optimization, which uses a point estimate of the true state as if the estimate were accurate. It then extends this idea to as-if maximin and minimax-regret decisions, which use point estimates of some features of the true state as if they were accurate. The paper primarily uses finite-sample maximum regret to evaluate the performance of inference-based SDFs. To illustrate abstract ideas, it presents specific findings concerning treatment choice and point prediction with sample data.