  The paper proposes a supervised machine learning algorithm to uncover
treatment effect heterogeneity in classical regression discontinuity (RD)
designs. Extending Athey and Imbens (2016), I develop a criterion for building
an honest "regression discontinuity tree", where each leaf of the tree contains
the RD estimate of a treatment (assigned by a common cutoff rule) conditional
on the values of some pre-treatment covariates. It is a priori unknown which
covariates are relevant for capturing treatment effect heterogeneity, and it is
the task of the algorithm to discover them, without invalidating inference. I
study the performance of the method through Monte Carlo simulations and apply
it to the data set compiled by Pop-Eleches and Urquiola (2013) to uncover
various sources of heterogeneity in the impact of attending a better secondary
school in Romania.
