The E-measure

TL;DR

The paper introduces the E-measure, a novel evidence measure generalizing the E-value, which is closed under infimums and useful for hypothesis testing, decision making, and evidence aggregation.

Contribution

It proposes the E-measure as a unique, non-dominated evidence measure compatible with logical implications, applicable to various statistical and decision-making contexts.

Findings

E-measures are the only non-dominated measures closed under intersections.

They provide uniform bounds on decisions and control false evidence rate without multiplicity correction.

E-measures enable a frequentist update from E-prior to E-posterior.

Abstract

We introduce the E-measure: a measure-like generalization of the E-value to a class of hypotheses. Unlike classical measures, E-measures are closed under infimums instead of addition. They arise from a compatibility axiom with logical implications, that there should be at least as much evidence against more specific hypotheses. We show that E-measures are the only non-dominated such objects, if the hypothesis class is closed under intersections. We propose to use the E-measure to present all the relevant evidence for a problem, where the relevance is captured by the choice of hypothesis class. We showcase this by applying the E-measure to decision making, inducing a hypothesis class from the uncertain consequences of decisions. This results in uniform E-consequence bounds on decisions, which nest high-probability loss bounds. Correcting for multiplicity, we consider 'familywise evidence' and 'false evidence rate' control, generalizing from errors and discoveries to continuous evidence. Remarkably, E-measures control these without multiplicity correction if the hypothesis class is intersection-closed. Moreover, we obtain a 'frequentist' notion of updating from E-prior to E-posterior. Abstracting the notion of a 'hypothesis', we advocate for using E-measures for any unknown quantity, leading to predictive E-measures.