influence function

Causal inference promises to answer “what if” questions from observational data. But the standard approach — specify a parametric model, fit it, report the estimate — breaks down under misspecification. And misspecification is the norm, not the exception.

Debiased machine learning (DML) is a generic recipe. The idea behind it is adding a correction term to the plug-in estimator of the functional, which leads to properties such as semi-parametric efficiency, double robustness, and Neyman orthogonality.