Source Condition Double Robust Inference on Functionals of Inverse Problems

Andrew Bennett; Nathan Kallus; Xiaojie Mao; Whitney Newey; Vasilis Syrgkanis; Masatoshi Uehara

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arXiv · 2307.13793

Source Condition Double Robust Inference on Functionals of Inverse Problems

Andrew Bennett·Nathan Kallus·Xiaojie Mao·Whitney Newey·Vasilis Syrgkanis·Masatoshi Uehara

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Abstract

We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems. Any such parameter admits a doubly robust representation that depends on the solution to a dual linear inverse problem, where the dual solution can be thought as a generalization of the inverse propensity function. We provide the first source condition double robust inference method that ensures asymptotic normality around the parameter of interest as long as either the primal or the dual inverse problem is sufficiently well-posed, without knowledge of which inverse problem is the more well-posed one. Our result is enabled by novel guarantees for iterated Tikhonov regularized adversarial estimators for linear inverse problems, over general hypothesis spaces, which are developments of independent interest.

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Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara. 2023-07-25. Source Condition Double Robust Inference on Functionals of Inverse Problems. https://arxiv.org/abs/2307.13793

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