2020/08/15 by Julio Michael Stern, Stern, Julio Michael, Marcos Antonio Simplicio +5
Computer Science · Mathematics · Medicine · Psychology · #Adversarial Robustness in Machine Learning #Artificial intelligence #Causal inference #Code (set theory) #Computer science #Data science #E.3 #Econometrics #FOS: Computer and information sciences #G.3 #G.4 #Inference #Java #K.4 #K.5 #Machine learning #Mathematics #Medicine #Other Statistics (stat.OT) #Privacy-Preserving Technologies in Data #Programming language #Psychology #Randomization #Randomized controlled trial #Spurious relationship #Statistical Methods in Clinical Trials #Statistical inference #Statistics #stat.OT
paper · pdf · doi:10.48550/arxiv.2008.06709
p.399-418 in Jose Acacio de Barros and Decio Krause, eds. (2020). A True Polymath: A Tribute to Francisco Antonio Doria. Rickmansworth, UK: College Publications. ISBN: 978-1-84890-351-7
openalex publication_date 2020/08/15 · arxiv created 2020/12/18 · arxiv updated 2020/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Randomization procedures are used in legal and statistical applications, aiming to shield important decisions from spurious influences. This article gives an intuitive introduction to randomization and examines some intended consequences of its use related to truthful statistical inference and fair legal judgment. This article also presents an open-code Java implementation for a cryptographically secure, statistically reliable, transparent, traceable, and fully auditable randomization tool.