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.

翻译：推荐人的业务目标,如增加销售量等,与建议的因果效果相一致。先前的因果效果推荐人采用因果推算反常态评分(IPS),但IPS容易出现很大差异。匹配估量是因果推算领域的另一个代表性方法。它不使用倾向性,因此没有上述差异问题。在这项工作中,我们将传统的邻里推荐方法与匹配的估量者统一起来,并为建议的因果效果制定稳健的排序方法。我们的实验表明,拟议的方法在因果效果的评分方面超过了各种基线。结果显示,拟议的方法可以比以前的推荐者实现更多的销售和用户参与。