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This paper introduces a framework using common-witness certificates and sharp feature bounds to audit counterfactual images, addressing the global-witness gap in structured image editors through theoretical and experimental validation.
This paper presents a partial-identification approach to use prior RCT data to bound the causal effect of a new ML model, leveraging assumptions about counterfactual correctness and subgroup predictive accuracy to yield more informative bounds.