Balanced and Robust Randomized Treatment Assignments: The Finite Selection Model

Oregon Convention Center 

The Finite Selection Model (FSM) was developed in the 1970s to design the RAND Health Insurance Experiment (HIE), one of the largest social science experiments conducted in the U.S. The idea behind the FSM is that each treatment group takes turns selecting units in a fair and random order to optimize a common criterion. At each of its turns, a treatment group selects the available unit that maximally improves the combined quality of its resulting group of units in terms of the criterion. In the HIE and beyond, we revisit, formalize, and extend the FSM as a general tool for balanced and efficient experimental design with multiple treatments. Leveraging the idea of D-optimality, we propose and analyze a new selection criterion in the FSM. The FSM using the D-optimal selection function has no tuning parameters, is affine invariant, and when appropriate, retrieves several classical designs such as randomized block and matched-pair designs. We demonstrate FSM's performance in a case study based on the HIE and in ten randomized studies from the health and social sciences. We recommend the FSM be considered in experimental design for its conceptual simplicity, efficiency, and robustness.