The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or exceeds a cutoff value. The RD design provides a "locally-randomized experiment" under remarkably mild conditions, so that the causal effect of treatment outcomes versus non-treatment outcomes can be identified and estimated at the cutoff (Lee, 2008). Such effect estimates are similar to those of a randomized study (Goldberger, 2008/1972). As a result, since 1997, at least 74 RD-based empirical studies have emerged in the ?fields of education, political science, psychology, economics, statistics, criminology, and health science (see van der Klaauw, 2008; Lee & Lemieux, 2010; Bloom, 2012; Wong et al. 2013; Li et al., 2013). Polynomial and local linear models are standard for RD designs (Bloom, 2012; Imbens & Lemieux, 2008). However, these models can produce biased causal effect estimates, due to the presence of outliers of treatment outcomes; and/or due to incorrect choices of the bandwidth parameter for the local linear model. Currently, the correct choice of bandwidth has only been justified by large-sample theory (Imbens & Kalyanaraman, 2012), and the local linear model for quantile regression (Frandsen et al., 2012) suffers from the "quantile crossing" problem. The authors introduce a novel formulation of their Bayesian nonparametric regression model (BLIND, 2012), which provides causal inference for RD designs. It is an infi?nite-mixture model, that allows the entire probability density of the outcome variable to change ?flexibly as a function of the assignment variable. Moreover, the Bayesian model can provide inferences of causal effects, in terms of how the treatment variable impacts the mean, variance, a quantile, distribution function, probability density, hazard function, and/or any other chosen functional of the outcome variable. Moreover, the accurate causal effect estimation relies on a predictively-accurate model for the data. The Bayesian nonparametric regression model attained best overall predictive performance, over many real data sets, compared to many other regression models (BLIND, 2012). Finally, the authors illustrate their Bayesian model through the causal analysis of two real educational data sets. Figures are appended.