Abstract and Keywords
Two evaluation designs are widely accepted as yielding results that are causally interpretable: the randomized experiment (RE) and the regression-discontinuity design (RDD). This paper explores theoretical and practical similarities between these two designs that have led some researchers to view them as “close cousins.” We also examine important differences between the designs. We conclude that the theoretical strength and possibility for unbiased implementation in practice warrant the privileged position these two designs hold among researchers concerned with the causal effects of interventions. However, the advantage in statistical power, more generally interpretable effect estimates, and straightforward approach to statistical modeling lead us to advise researchers to choose REs over RDDs when all else is equal.
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