  For non-randomized studies, the regression discontinuity design (RDD) can be
used to identify and estimate causal effects from a "locally-randomized"
subgroup of subjects, under relatively mild conditions. However, current models
focus causal inferences on the impact of the treatment (versus non-treatment)
variable on the mean of the dependent variable, via linear regression. For
RDDs, we propose a flexible Bayesian nonparametric regression model that can
provide accurate estimates of causal effects, in terms of the predictive mean,
variance, quantile, probability density, distribution function, or any other
chosen function of the outcome variable. We illustrate the model through the
analysis of two real educational data sets, involving (resp.) a sharp RDD and a
fuzzy RDD.
