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