# Causal Inference for Recommendation: Foundations, Methods and Applications

arXiv: https://arxiv.org/abs/2301.04016 · HTML: https://mlsanity.com/paper/2301.04016/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2023.

## Built on by (most cited first, 4 of 4)

- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [Stable Time Series Prediction of Enterprise Carbon Emissions Based on Causal Inference](https://mlsanity.com/paper/2602.00775/) (2026)
- [LILI clustering algorithm: Limit Inferior Leaf Interval Integrated into Causal Forest for Causal Interference](https://mlsanity.com/paper/2507.03271/) (2025)
- [MedicalRec: Medical recommender system for image classification without retraining](https://mlsanity.com/paper/2606.07553/) (2026)

## Abstract (arXiv)

Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, relying solely on correlation without considering the underlying causal mechanism may lead to various practical issues such as fairness, explainability, robustness, bias, echo chamber and controllability problems. Therefore, researchers in related area have begun incorporating causality into recommendation systems to address these issues. In this survey, we review the existing literature on causal inference in recommender systems. We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems. Finally, we discuss open problems and future directions in the field of causal inference for recommendations.