Causal inference

Sharp minimax theory for structure-agnostic estimation of general linear functionals, including optimality of first-order debiasing and doubly robust estimators.

We consider structure-agnostic causal inference that estimates treatment effect estimation using black-box ML estimates of nuisance functions, and show that the celebrated DML is optimal when the treatment noise is Gaussian. When the noise is non-Gaussian, we propose ACE, a novel class of higher-order structure-agnostic estimators.

We show that first-order debiasing of black-box ML estimators is optimal for estimating average treatment effect.