Before revision:  We prove a central limit theorem and propose a computationally efficient construction for confidence intervals  .
After revision:  We prove a central limit theorem and propose a computationally efficient construction for confidence intervals . Moving to a causal inference application, we discuss the merits of local regression adjustments for heterogeneous treatment effect estimation, and give an example on a dataset exploring the effect word choice has on attitudes to the social safety net. Last, we include simulation results on real and generated data .