Debiased Inference of Average Partial Effects in Single-Index Models: Comment on Wooldridge and Zhu

There has recently been a considerable amount of interest in developing methods for statistical inference in high-dimensional regimes with more covariates than data points (Javanmard and Montanari 2014; van de Geer et al. 2014; Zhang and Zhang 2014; Belloni et al. 2017; Athey, Imbens, and Wager 2018). Wooldridge and Zhu build on this literature, and propose a new method for inference about average partial effects (APEs) in high-dimensional probit models; they then extend their approach to nonlinear panels with correlated random effects (Wooldridge 2010). This is a valuable result, with many potential application areas.