  The business objectives of recommenders, such as increasing sales, are
aligned with the causal effect of recommendations. Previous recommenders
targeting for the causal effect employ the inverse propensity scoring (IPS) in
causal inference. However, IPS is prone to suffer from high variance. The
matching estimator is another representative method in causal inference field.
It does not use propensity and hence free from the above variance problem. In
this work, we unify traditional neighborhood recommendation methods with the
matching estimator, and develop robust ranking methods for the causal effect of
recommendations. Our experiments demonstrate that the proposed methods
outperform various baselines in ranking metrics for the causal effect. The
results suggest that the proposed methods can achieve more sales and user
engagement than previous recommenders.
