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This work examines ways to make the best use of limited resources when selecting individuals to follow up in a longitudinal study estimating causal effects. In the setting under consideration, covariate information is available for all individuals but outcomes have not yet been collected and may be expensive to gather, and thus only a subset of the comparison subjects will be followed. Expressions in Rubin and Thomas (1996, 2000) show the benefits that can be obtained, in terms of reduced bias and variance of the estimated treatment effect, of selecting comparison individuals well-matched to those in the treated group, as compared to a random sample of comparison individuals. We primarily consider non-experimental settings but also consider implications for randomized trials. The methods are illustrated using data from the Johns Hopkins University Baltimore Prevention Program, which included data collection from age 6 to young adulthood of participants in an evaluation of two early elementary-school based universal prevention programs.
Stuart et al. (Fri,) studied this question.