In recent years, the regression discontinuity design (RDD) has gained widespread recognition as a quasi-experimental method that when used correctly, can produce internally valid estimates of causal effects of a treatment, a program or an intervention (hereafter referred to as treatment effects). In an RDD study, subjects or groups of subjects (e.g. students or schools) are rated according to a numeric index (a performance indicator, poverty measure, etc.) and treatment assignment is determined by whether one's rating falls above or below an exogenously defined cut-point value of the rating. RDDs have been used to estimate causal effects in a variety of contexts (e.g. for a list of more than 75 studies in the contexts of education, labor markets, political economy, health, crime and more see Lee & Lemieux, 2009), and research on their statistical properties has provided theoretical justification and empirical verification of their internal validity. This paper explores the conditions that limit the generalizability of RDD estimates and concludes that that in many cases, generalizability is much greater than often believed. It also presents an empirical approach for quantifying the generalizability of RDD findings so that more information can be brought to bear on this important issue. (Contains 1 figure.)