  We compare the performance of standard nearest-neighbor propensity score
matching with that of an analogous Bayesian propensity score matching
procedure. We show that the Bayesian approach makes better use of available
information, as it makes less arbitrary decisions about which observations to
drop and which ones to keep in the matched sample. We conduct a simulation
study to evaluate the performance of standard and Bayesian nearest-neighbor
matching when matching is done with and without replacement. We then use both
methods to replicate a recent study about the impact of land reform on
guerrilla activity in Colombia.
