# Causal Embeddings for Recommendation: An Extended Abstract

arXiv: https://arxiv.org/abs/1904.05165 · HTML: https://mlsanity.com/paper/1904.05165/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering](https://mlsanity.com/paper/2305.12768/) (2023)
- [Causally-Informed Reinforcement Learning for Adaptive Emotion-Aware Social Media Recommendation](https://mlsanity.com/paper/2511.14768/) (2025)

## Abstract (arXiv)

Recommendations are commonly used to modify user's natural behavior, for example, increasing product sales or the time spent on a website. This results in a gap between the ultimate business objective and the classical setup where recommendations are optimized to be coherent with past user behavior. To bridge this gap, we propose a new learning setup for recommendation that optimizes for the Incremental Treatment Effect (ITE) of the policy. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy and propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.