2025/01/01 by Ezequiel Smucler, James M. Robins, A Rotnitzky · 1 voice · 1 citation
Engineering · #Industrial and Mining Safety
paper · doi:10.1093/biomet/asaf067
openalex publication_date 2025/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
This paper examines the construction of confidence sets for parameters defined as linear functionals of a function of [Formula: see text] and [Formula: see text] whose conditional mean given [Formula: see text] and [Formula: see text] equals the conditional mean of another variable [Formula: see text] given [Formula: see text] and [Formula: see text]. Many estimands of interest in causal inference can be expressed in this form, including the average treatment effect in proximal causal inference and treatment effect contrasts in instrumental variable models. We derive a necessary condition for a confidence set to be uniformly valid over a model that allows for the dependence between [Formula: see text] and [Formula: see text] given [Formula: see text] to be arbitrarily weak. We show that, for any such confidence set, there must exist some laws in the model under which, with high probability, the confidence set has a diameter greater than or equal to the diameter of the parameter's range. In particular, consistent with the weak instrument literature, Wald confidence intervals are not uniformly valid over the aforementioned model when the parameter's range is infinite. Furthermore, we argue that inverting the score test, a successful approach in that literature, generally fails for the broader class of parameters considered here. We present a method for constructing uniformly valid confidence sets when all variables, but possibly [Formula: see text], are binary, discuss its limitations and emphasize that developing valid confidence sets for the class of parameters considered here remains an open problem.