# Causality-Aware Neighborhood Methods for Recommender Systems

arXiv: https://arxiv.org/abs/2012.09442 · HTML: https://mlsanity.com/paper/2012.09442/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [The Bandwagon Effect: Not Just Another Bias](https://mlsanity.com/paper/2206.12701/) (2022)
- [Online Evaluation Methods for the Causal Effect of Recommendations](https://mlsanity.com/paper/2107.06630/) (2021)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)

## Abstract (arXiv)

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.