Actions Have Consequences: Detecting Outcome Performativity using Intervention Testing

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 Outcome Performativity using OPAB is achievable in numerous cases. Results also show the presence of regions of indistinguishability which describe settings where the allotted number of interventions are insufficient for detecting Outcome Performativity. The results of which have broader practical implications for the detectability of Outcome Performativity in settings where samples are scarce, cost-prohibitive or potentially unethical to obtain. The paper concludes with a case study on the efficacy of OPAB on the Open Bandits dataset, and provides directions for future work.

25 pages, 18 Figures. Accepted to the 29th International Conference on Discovery Science, 2026

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.26908 [cs.LG]

(or arXiv:2607.26908v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.26908 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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