Common-Witness Certificates and Sharp Feature Bounds for Counterfactual Image Auditing

Abstract:An image editor may satisfy every regional plausibility constraint separately even when no single latent explanation fits the complete output. We formalize this local-to-global failure using a common witness grade and witness nerve. The framework separates auditing from causal identification: shared exogeneity alone allows every coupling of the regime marginals, whereas an externally justified witness relation yields sharp partial-identification bounds for prespecified image features. Helly-type arguments provide short incompatibility certificates for quasiconvex losses, heterogeneous action strata, and finite witness atlases; a blocker-hypergraph formula gives exact repair counts. Simultaneous confidence regions for the regime marginals give finite-sample outer coverage of the complete identified interval. Controlled MNIST, Morpho-MNIST, and smallNORB studies demonstrate the predicted local-global separation, while synthetic experiments test sharp bounds, certificate recovery, and structured computation. The method audits a declared feature relation and does not identify unrestricted pixel-level counterfactuals.

Artificial Intelligence (cs.AI)

arXiv:2609.03973 [cs.AI]

(or arXiv:2609.03973v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.03973 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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