Abstract: Random forests are popular methods for classification and regression, and many different variants have been proposed. One interesting example is the Mondrian random forest, in which the underlying trees are constructed according to a Mondrian process. We give a novel central limit theorem for the estimates made by a Mondrian random forest in the regression setting. When combined with a bias characterization and a consistent variance estimator, this allows one to perform asymptotically valid statistical inference on the unknown regression function. We also provide a debiasing procedure for Mondrian random forests which allows them to achieve minimax-optimal estimation rates.