יום שישי, 31 ביולי 2026 LIVE
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כתבה arXiv cs.AI ·

Actions Have Consequences: Detecting Outcome Performativity using Intervention Testing

תקציר מקורי באנגליתarXiv:2607.26908v1 Announce Type: cross Abstract: In many domains such as Palliative Care, Credit Assignment and Recommender Systems, predictions may causally influence the outcomes they predict. This phenomena is known as Outcome Performativity. This paper formalises an approach for detecting Outcome Performativity using prediction intervention called Outcome Performativity A/B Detection (OPAB). OPAB enables the detection of Outcome Performativity by assessing the dissimilarity in outcome distributions produced by different predictions groups (interventions). If that dissimilarity is significant, Outcome Performativity is detected. We derive sample complexity bounds for OPAB under various Outcome Performative assumption classes which we empirically validate. Results show that detecting Ou
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