# Causal Embeddings for Recommendation: An Extended Abstract

## TL;DR

This paper introduces a novel recommendation learning framework that estimates the incremental treatment effect by predicting outcomes under random exposure, improving upon existing methods through causal embeddings and domain adaptation.

## Contribution

It proposes a new causal embedding approach for recommendation systems that learns from biased logged data to predict outcomes under random policies, bridging the gap between business objectives and traditional models.

## Key findings

- Significant improvements over state-of-the-art factorization methods.
- Effective domain adaptation from biased logged data to random recommendation outcomes.
- Demonstrated superiority of causal recommendation approaches.

## Abstract

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.