  Treatment heterogeneity is ubiquitous in many areas, motivating practitioners
to search for the optimal policy that maximizes the expected outcome based on
individualized characteristics. However, most existing policy learning methods
rely on weighting-based approaches, which may suffer from high instability in
observational studies. To enhance the robustness of the estimated policy, we
propose a matching-based estimator of the policy improvement upon a randomized
baseline. After correcting the conditional bias, we learn the optimal policy by
maximizing the estimate over a policy class. We derive a non-asymptotic high
probability bound for the regret of the learned policy and show that the
convergence rate is almost $1/\sqrt{n}$. The competitive finite sample
performance of the proposed method is demonstrated in extensive simulation
studies and a real data application.
