2021/05/05 by Ambarish Chattopadhyay, Chattopadhyay, Ambarish, Carl N. Morris +3
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2105.02393
openalex publication_date 2021/05/05 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
The original Finite Selection Model (FSM) was developed in the 1970s to\nenhance the design of the RAND Health Insurance Experiment (HIE; Newhouse et\nal. 1993). At the time of its development by Carl Morris (Morris 1979), there\nwere fundamental computational limitations to make the method widely available\nfor practitioners. Today, as randomized experiments increasingly become more\ncommon, there is a need for implementing experimental designs that are\nrandomized, balanced, robust, and easily applicable to several treatment\ngroups. To help address this problem, we revisit the original FSM under the\npotential outcome framework for causal inference and provide its first readily\navailable software implementation. In this paper, we provide an introduction to\nthe FSM and a step-by-step guide for its use in R.\n