Many methods for estimating conditional average treatment effects (CATEs) can be expressed as weighted pseudo-outcome regressions (PORs). 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. For example, we point out that R-Learning implicitly performs a POR with inverse-variance weights (IVWs). In the CATE setting, IVWs mitigate the instability associated with inverse-propensity weights, and lead to convenient simplifications of bias terms. We demonstrate the superior performance of IVWs in simulations, and derive convergence rates for IVWs that are, to our knowledge, the fastest yet shown without assuming knowledge of the covariate distribution.

翻译：许多用于估计条件平均处理效应（CATEs）的方法可被表述为加权伪结果回归（POR）。先前对POR技术的比较重点关注伪结果变换的选择。然而，我们认为影响性能的主导因素实际上是权重的选择。例如，我们指出R学习隐式地执行了带逆方差权重（IVWs）的POR。在CATE设定中，IVWs缓解了逆倾向权重相关的不稳定性，并简化了偏置项。我们通过模拟实验展示了IVWs的优越性能，并推导了其收敛速率——据我们所知，这是在不假设协变量分布已知的情况下目前最快的收敛速率。