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Debiased machine learning is a meta algorithm based on bias correction andsample splitting to calculate confidence intervals for functionals, i.e. scalarsummaries, of machine learning algorithms. For example, an analyst may desirethe confidence interval for a treatment effect estimated with a neural network.We provide a nonasymptotic debiased machine learning theorem that encompassesany global or local functional of any machine learning algorithm that satisfiesa few simple, interpretable conditions. Formally, we prove consistency,Gaussian approximation, and semiparametric efficiency by finite samplearguments. The rate of convergence is $n^{-1/2}$ for global functionals, and itdegrades gracefully for local functionals. Our results culminate in a simpleset of conditions that an analyst can use to translate modern learning theoryrates into traditional statistical inference. The conditions reveal a generaldouble robustness property for ill posed inverse problems.

A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees