  We consider off-policy evaluation (OPE) in continuous treatment settings,
such as personalized dose-finding. In OPE, one aims to estimate the mean
outcome under a new treatment decision rule using historical data generated by
a different decision rule. Most existing works on OPE focus on discrete
treatment settings. To handle continuous treatments, we develop a novel
estimation method for OPE using deep jump learning. The key ingredient of our
method lies in adaptively discretizing the treatment space using deep
discretization, by leveraging deep learning and multi-scale change point
detection. This allows us to apply existing OPE methods in discrete treatments
to handle continuous treatments. Our method is further justified by theoretical
results, simulations, and a real application to Warfarin Dosing.
