B0282
Title: Balanced and robust randomized treatment assignments: The finite selection model
Authors: Ambarish Chattopadhyay - Stanford University (United States) [presenting]
Carl Morris - Harvard University (United States)
Jose Zubizarreta - Harvard University (United States)
Abstract: The finite selection model (FSM) was developed in the 1970s for the design of the RAND health insurance experiment (HIE), one of the largest and most comprehensive social science experiments conducted in the U.S. The idea behind the FSM is that each treatment group takes its 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, the FSM is revisited, formalized, and extended as a general experimental design tool for causal inference. Leveraging the idea of D-optimality, a new selection criterion in the FSM is proposed and analyzed. 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. For multi-arm experiments, algorithms are proposed to generate a fair and random selection order of treatments. FSM's performance is demonstrated in a case study based on the HIE and in ten randomized studies from the health and social sciences.
