Randomized and Balanced Allocation of Units into Treatment Groups Using the Finite Selection Model for R

TL;DR

This paper revisits the Finite Selection Model (FSM) for randomized, balanced treatment assignment, providing a modern implementation in R to facilitate its use in experimental design.

Contribution

It introduces a new software implementation of FSM in R and offers a practical guide for its application in randomized experiments.

Findings

Provides an R package for FSM implementation

Demonstrates FSM's effectiveness in balanced treatment allocation

Enhances experimental design with a robust, accessible method

Abstract

The original Finite Selection Model (FSM) was developed in the 1970s to enhance the design of the RAND Health Insurance Experiment (HIE; Newhouse et al. 1993). At the time of its development by Carl Morris (Morris 1979), there were fundamental computational limitations to make the method widely available for practitioners. Today, as randomized experiments increasingly become more common, there is a need for implementing experimental designs that are randomized, balanced, robust, and easily applicable to several treatment groups. To help address this problem, we revisit the original FSM under the potential outcome framework for causal inference and provide its first readily available software implementation. In this paper, we provide an introduction to the FSM and a step-by-step guide for its use in R.