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.

翻译：我们的动机是阐明广受欢迎的“R学习器”的性能表现。与许多其他估计条件平均处理效应的方法类似，R学习可以表示为加权伪结果回归。以往对伪结果回归技术的比较研究主要关注伪结果变换的选择。然而，我们认为实际影响性能的主导因素在于权重的选择。具体而言，我们论证R学习隐式地执行了一种逆方差加权的伪结果回归形式。这类权重能够稳定回归过程，并有助于简化偏差项的表达式。