# A Model-Agnostic Causal Learning Framework for Recommendation using Search Data

arXiv: https://arxiv.org/abs/2202.04514 · HTML: https://mlsanity.com/paper/2202.04514/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2022.

## Built on by (most cited first, 17 of 17)

- [When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation](https://mlsanity.com/paper/2305.10822/) (2023)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [An Unified Search and Recommendation Foundation Model for Cold-Start Scenario](https://mlsanity.com/paper/2309.08939/) (2023)
- [Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao](https://mlsanity.com/paper/2306.03527/) (2023)
- [Instrumental Variables in Causal Inference and Machine Learning: A Survey](https://mlsanity.com/paper/2212.05778/) (2022)
- [Debiasing Recommendation by Learning Identifiable Latent Confounders](https://mlsanity.com/paper/2302.05052/) (2023)
- [CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state](https://mlsanity.com/paper/2404.12228/) (2024)
- [CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning](https://mlsanity.com/paper/2403.00880/) (2024)
- [RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation](https://mlsanity.com/paper/2309.10469/) (2023)
- [Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation](https://mlsanity.com/paper/2403.03578/) (2024)
- [Interaction-Data-guided Conditional Instrumental Variables for Debiasing Recommender Systems](https://mlsanity.com/paper/2408.09651/) (2024)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [Causal Intervention for Fairness in Multi-behavior Recommendation](https://mlsanity.com/paper/2209.04589/) (2022)
- [Separating and Learning Latent Confounders to Enhancing User Preferences Modeling](https://mlsanity.com/paper/2311.03381/) (2023)
- [Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression](https://mlsanity.com/paper/2509.22794/) (2025)
- [Causality Enhancement for Cross-Domain Recommendation](https://mlsanity.com/paper/2510.14641/) (2025)
- [CafeMed: Causal Attention Fusion Enhanced Medication Recommendation](https://mlsanity.com/paper/2511.14064/) (2025)

## Abstract (arXiv)

Machine-learning based recommender systems(RSs) has become an effective means to help people automatically discover their interests. Existing models often represent the rich information for recommendation, such as items, users, and contexts, as embedding vectors and leverage them to predict users' feedback. In the view of causal analysis, the associations between these embedding vectors and users' feedback are a mixture of the causal part that describes why an item is preferred by a user, and the non-causal part that merely reflects the statistical dependencies between users and items, for example, the exposure mechanism, public opinions, display position, etc. However, existing RSs mostly ignored the striking differences between the causal parts and non-causal parts when using these embedding vectors. In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results. Specifically, we jointly consider users' behaviors in search scenarios and recommendation scenarios. Adopting the concepts in causal analysis, we embed users' search behaviors as instrumental variables (IVs), to help decompose original embedding vectors in recommendation, i.e., treatments. IV4Rec then combines the two parts through deep neural networks and uses the combined results for recommendation. IV4Rec is model-agnostic and can be applied to a number of existing RSs such as DIN and NRHUB. Experimental results on both public and proprietary industrial datasets demonstrate that IV4Rec consistently enhances RSs and outperforms a framework that jointly considers search and recommendation.