in this talk, i will discuss semi-parametric estimation when nuisance parameters cannot be estimated consistently, focusing in particular on the estimation of average treatment effects, conditional correlations, and linear effects under high-dimensional glm specifications. in this challenging regime, even standard doubly-robust estimators can be inconsistent. i describe novel approaches which enjoy consistency guarantees for low-dimensional target parameters even though standard approaches fail. for some target parameters, these guarantees can also be used for inference. finally, i will provide my perspective on the broader implications of this work for designing methods which are less sensitive to biases from high-dimensional prediction models.