  Counterfactual learning for dealing with missing-not-at-random data (MNAR) is
an intriguing topic in the recommendation literature since MNAR data are
ubiquitous in modern recommender systems. Missing-at-random (MAR) data, namely
randomized controlled trials (RCTs), are usually required by most previous
counterfactual learning methods for debiasing learning. However, the execution
of RCTs is extraordinarily expensive in practice. To circumvent the use of
RCTs, we build an information-theoretic counterfactual variational information
bottleneck (CVIB), as an alternative for debiasing learning without RCTs. By
separating the task-aware mutual information term in the original information
bottleneck Lagrangian into factual and counterfactual parts, we derive a
contrastive information loss and an additional output confidence penalty, which
facilitates balanced learning between the factual and counterfactual domains.
Empirical evaluation on real-world datasets shows that our CVIB significantly
enhances both shallow and deep models, which sheds light on counterfactual
learning in recommendation that goes beyond RCTs.
