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Abstract:
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A recently introduced method called floodgate can provide asymptotic inference for the importance of a covariate in a (possibly high-dimensional) regression. The measure of importance that serves as the inferential target is interpretable yet completely model-free, capturing arbitrary nonlinearities and interactions in the conditional relationship between a covariate and the response given the other covariates. The floodgate method is based on the novel idea of a floodgate function, which gives a flexible deterministic yet unobservable lower-bound for the inferential target, but is much easier to provide inference for than the original target. This talk will show how the floodgate approach can be generalized to infer many other targets of interest for high-dimensional regression applications.
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