# The Deconfounded Recommender: A Causal Inference Approach to Recommendation

arXiv: https://arxiv.org/abs/1808.06581 · HTML: https://mlsanity.com/paper/1808.06581/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2018.

## Built on by (most cited first, 19 of 19)

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- [Identifiable Generative Models for Missing Not at Random Data Imputation](https://mlsanity.com/paper/2110.14708/) (2021)
- [Sequential Deconfounding for Causal Inference with Unobserved Confounders](https://mlsanity.com/paper/2104.09323/) (2021)
- [Respecting Transfer Gap in Knowledge Distillation](https://mlsanity.com/paper/2210.12787/) (2022)
- [Causal Incremental Graph Convolution for Recommender System Retraining](https://mlsanity.com/paper/2108.06889/) (2021)
- [Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation](https://mlsanity.com/paper/2202.02576/) (2022)
- [Conformal Counterfactual Inference under Hidden Confounding](https://mlsanity.com/paper/2405.12387/) (2024)
- [Be Causal: De-biasing Social Network Confounding in Recommendation](https://mlsanity.com/paper/2105.07775/) (2021)
- [Causal Learning for Socially Responsible AI](https://mlsanity.com/paper/2104.12278/) (2021)
- [In the Eye of the Beholder: Robust Prediction with Causal User Modeling](https://mlsanity.com/paper/2206.00416/) (2022)
- [Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning](https://mlsanity.com/paper/2412.05783/) (2024)
- [Bilateral Self-unbiased Learning from Biased Implicit Feedback](https://mlsanity.com/paper/2207.12660/) (2022)
- [Feature Recommendation for Structural Equation Model Discovery in Process Mining](https://mlsanity.com/paper/2108.07795/) (2021)
- [Hybrid Feature- and Similarity-Based Models for Joint Prediction and Interpretation](https://mlsanity.com/paper/2204.06076/) (2022)
- [Ranking Policy Learning via Marketplace Expected Value Estimation From Observational Data](https://mlsanity.com/paper/2410.04568/) (2024)
- [Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity](https://mlsanity.com/paper/2505.14310/) (2025)
- [Identifiable Deep Latent Variable Models for MNAR Data](https://mlsanity.com/paper/2603.24771/) (2026)
- [PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation](https://mlsanity.com/paper/2605.21752/) (2026)
- [From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation](https://mlsanity.com/paper/2608.10182/) (2026)

## Abstract (arXiv)

The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question---for each user and movie, what would the rating be if we "forced" the user to watch the movie? To this end, we develop a causal approach to recommendation, one where watching a movie is a "treatment" and a user's rating is an "outcome." The problem is there may be unobserved confounders, variables that affect both which movies the users watch and how they rate them; unobserved confounders impede causal predictions with observational data. To solve this problem, we develop the deconfounded recommender, a way to use classical recommendation models for causal recommendation. Following Wang & Blei [23], the deconfounded recommender involves two probabilistic models. The first models which movies the users watch; it provides a substitute for the unobserved confounders. The second one models how each user rates each movie; it employs the substitute to help account for confounders. This two-stage approach removes bias due to confounding. It improves recommendation and enjoys stable performance against interventions on test sets.