Regression Discontinuity Design

Abstract

Social scientists search for interventions in the real world that approximate the conditions of an experiment. One form of such natural experiments that is increasingly used in social science research is regression discontinuity (RD). RD designs are possible when there are thresholds that cause large changes in the assignment of treatments on the basis of small differences in a variable. For example, a high school junior in the state of Pennsylvania who scored 214 out of 240 on the 2012 PSAT test received the treatment of being a National Merit Semi-Finalist, whereas a comparable student who scored 213 did not. The intuition behind a RD design is that we often can learn something about the effects of a treatment by comparing observations that barely receive a treatment (e.g., individuals with scores of 214 and just above on the PSAT) to observations that barely miss receiving a treatment (e.g., individuals who score 213 and just below on the PSAT). We discuss the assumptions under which the effects of treatment that are assigned based on a discontinuous threshold can be estimated using a RD design. We then illustrate how graphical analysis can be used to illustrate whether these assumptions are likely to hold. We conclude by discussing two examples of cutting-edge research that employs RD designs and discussing areas of future research. Keywords: regression discontinuity; natural experiments; treatment effects; selection bias