# The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects

**Authors:** Aaron Fisher  
**arXiv:** [2307.09700](https://arxiv.org/abs/2307.09700)  
**Date:** 2023-07-19  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/2307.09700v1/

## Abstract

Our motivation is to shed light the performance of the widely popular "R-Learner." Like many other methods for estimating conditional average treatment effects (CATEs), R-Learning can be expressed as a weighted pseudo-outcome regression (POR). Previous comparisons of POR techniques have paid careful attention to the choice of pseudo-outcome transformation. However, we argue that the dominant driver of performance is actually the choice of weights. Specifically, we argue that R-Learning implicitly performs an inverse-variance weighted form of POR. These weights stabilize the regression and allow for convenient simplifications of bias terms.