  We propose the first boosting algorithm for off-policy learning from logged
bandit feedback. Unlike existing boosting methods for supervised learning, our
algorithm directly optimizes an estimate of the policy's expected reward. We
analyze this algorithm and prove that the excess empirical risk decreases
(possibly exponentially fast) with each round of boosting, provided a ''weak''
learning condition is satisfied by the base learner. We further show how to
reduce the base learner to supervised learning, which opens up a broad range of
readily available base learners with practical benefits, such as decision
trees. Experiments indicate that our algorithm inherits many desirable
properties of tree-based boosting algorithms (e.g., robustness to feature
scaling and hyperparameter tuning), and that it can outperform off-policy
learning with deep neural networks as well as methods that simply regress on
the observed rewards.
