  This work studies the problem of learning unbiased algorithms from biased
feedback for recommendation. We address this problem from a novel distribution
shift perspective. Recent works in unbiased recommendation have advanced the
state-of-the-art with various techniques such as re-weighting, multi-task
learning, and meta-learning. Despite their empirical successes, most of them
lack theoretical guarantees, forming non-negligible gaps between theories and
recent algorithms. In this paper, we propose a theoretical understanding of why
existing unbiased learning objectives work for unbiased recommendation. We
establish a close connection between unbiased recommendation and distribution
shift, which shows that existing unbiased learning objectives implicitly align
biased training and unbiased test distributions. Built upon this connection, we
develop two generalization bounds for existing unbiased learning methods and
analyze their learning behavior. Besides, as a result of the distribution
shift, we further propose a principled framework, Adversarial Self-Training
(AST), for unbiased recommendation. Extensive experiments on real-world and
semi-synthetic datasets demonstrate the effectiveness of AST.
