  This paper introduces BART-RDD, a sum-of-trees regression model built around
a novel regression tree prior, which incorporates the special covariate
structure of regression discontinuity designs. Specifically, the tree splitting
process is constrained to ensure overlap within a narrow band surrounding the
running variable cutoff value, where the treatment effect is identified. It is
shown that unmodified BART-based models estimate RDD treatment effects poorly,
while our modified model accurately recovers treatment effects at the cutoff.
Specifically, BART-RDD is perhaps the first RDD method that effectively learns
conditional average treatment effects. The new method is investigated in
thorough simulation studies as well as an empirical application looking at the
effect of academic probation on student performance in subsequent terms (Lindo
et al., 2010).
