Our framework can be viewed as inference on low-dimensional nonparametric functions in the presence of high-dimensional nuisance function (where dimensionality refers to the number of covariates). Specifically, we consider the setting where we have a signal $Y=Y(\eta_0)$ that is an unbiased predictor of causal/structural objects like treatment effect, structural derivative, outcome given treatment, and others, conditional on a set of very high dimensional controls $Z$... (read more)

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