Before revision: This paper contributes to the literature on high-dimensional sparse M-estimation by allowing the loss function to depend on a functional nuisance parameter, which we estimate by modern machine learning tools.
After revision: This paper contributes to the literature on high-dimensional sparse M-estimation by allowing the loss function to depend on a nuisance function, such as the propensity score or the conditional choice probability, which we estimate by modern machine learning tools.