Doubly robust inference via calibration

Understand

Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions.

• While consistency requires only one of two nuisance functions to be estimated consistently, asymptotic normality for linear functionals typically requires sufficiently fast convergence of both.
• We address this mismatch by showing that calibrating the nuisance estimators within a doubly robust procedure can yield doubly robust asymptotic normality.
• We introduce the idea of calibrated debiased machine learning (DML) and propose a specific implementation in which standard DML is augmented with a simple isotonic regression adjustment.