The paper constructs minimax-bias estimators and uniformly valid confidence intervals for weighted estimands by bounding differences via parameter heterogeneity and weight distance.
arXiv preprint arXiv:2502.17830 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Hypothesis tests and confidence intervals are ubiquitous in empirical research, yet their connection to subsequent decision-making is often unclear. We develop a theory of certified decisions that pairs recommended decisions with inferential guarantees. Specifically, we attach P-certificates -- upper bounds on loss that hold with probability at least $1-\alpha$ -- to recommended actions. We show that such certificates allow "safe," risk-controlling adoption decisions for ambiguity-averse downstream decision-makers. We further prove that it is without loss to limit attention to P-certificates arising as minimax decisions over confidence sets, or what Manski (2021) terms "as-if decisions with a set estimate." A parallel argument applies to E-certified decisions obtained from e-values in settings with unbounded loss.
years
2026 3representative citing papers
E-measures generalize E-values to intersection-closed hypothesis classes, yielding uniform evidence bounds, automatic familywise evidence control without multiplicity correction, and a frequentist E-prior to E-posterior update.
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Robust Inference for Weighted Estimands
The paper constructs minimax-bias estimators and uniformly valid confidence intervals for weighted estimands by bounding differences via parameter heterogeneity and weight distance.
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The E-measure
E-measures generalize E-values to intersection-closed hypothesis classes, yielding uniform evidence bounds, automatic familywise evidence control without multiplicity correction, and a frequentist E-prior to E-posterior update.
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