  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.
