# A Survey on Causal Inference for Recommendation

arXiv: https://arxiv.org/abs/2303.11666 · HTML: https://mlsanity.com/paper/2303.11666/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2023.

## Built on by (most cited first, 22 of 22)

- [Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation](https://mlsanity.com/paper/2412.11654/) (2024)
- [Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random](https://mlsanity.com/paper/2403.00817/) (2024)
- [Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models](https://mlsanity.com/paper/2404.08695/) (2024)
- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2502.15687/) (2024)
- [Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference](https://mlsanity.com/paper/2404.19620/) (2024)
- [Causal Structure Representation Learning of Confounders in Latent Space for Recommendation](https://mlsanity.com/paper/2311.03382/) (2023)
- [DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback](https://mlsanity.com/paper/2405.15280/) (2024)
- [Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness](https://mlsanity.com/paper/2603.20775/) (2026)
- [MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation](https://mlsanity.com/paper/2603.25126/) (2026)
- [Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments](https://mlsanity.com/paper/2411.01498/) (2024)
- [Personalized Causal Graph Reasoning for LLMs: An Implementation for Dietary Recommendations](https://mlsanity.com/paper/2503.00134/) (2025)
- [Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity](https://mlsanity.com/paper/2505.14310/) (2025)
- [Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift](https://mlsanity.com/paper/2507.05412/) (2025)
- [Causality Enhancement for Cross-Domain Recommendation](https://mlsanity.com/paper/2510.14641/) (2025)
- [Mitigating Gender Bias in Depression Detection via Counterfactual Inference](https://mlsanity.com/paper/2512.01834/) (2025)
- [CIRR: Causal-Invariant Retrieval-Augmented Recommendation with Faithful Explanations under Distribution Shift](https://mlsanity.com/paper/2512.18683/) (2025)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)
- [Causal Discovery Should Embrace the Wisdom of the Crowd](https://mlsanity.com/paper/2603.02678/) (2026)
- [CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems](https://mlsanity.com/paper/2604.23022/) (2026)
- [LLM-Based Intelligent Notification Composition: From Static Personalization to Context-Aware Persuasive Messaging](https://mlsanity.com/paper/2605.16264/) (2026)
- [GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation](https://mlsanity.com/paper/2609.02162/) (2026)

## Abstract (arXiv)

Causal inference has recently garnered significant interest among recommender system (RS) researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields. It offers a framework to model the causality in recommender systems like confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation. Although there are already some valuable surveys on causal recommendations, they typically classify approaches based on the practical issues faced in RS, a classification that may disperse and fragment the unified causal theories. Considering RS researchers' unfamiliarity with causality, it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective, thereby facilitating a deeper integration of causal inference in RS. This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal strategy. Firstly, we introduce the fundamental concepts of causal inference as the basis of the following review. Subsequently, we propose a novel theory-driven taxonomy, categorizing existing methods based on the causal theory employed - namely, those based on the potential outcome framework, the structural causal model, and general counterfactuals. The review then delves into the technical details of how existing methods apply causal inference to address particular recommender issues. Finally, we highlight some promising directions for future research in this field. Representative papers and open-source resources will be progressively available at https://github.com/Chrissie-Law/Causal-Inference-for-Recommendation.