Causal Inference

It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation'
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.
Jikai Jin, Lester Mackey, Vasilis Syrgkanis
ArXiv
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
We show that first-order debiasing of black-box ML estimators is optimal for estimating average treatment effect.
Jikai Jin, Vasilis Syrgkanis
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