行为有其后果：通过干预测试检测结果述行性
Actions Have Consequences: Detecting Outcome Performativity using Intervention Testing
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中文总结 AI 辅助
本文提出OPAB方法，通过评估不同预测组干预下的结果分布差异性检测结果述行性，推导并验证其样本复杂度边界，在Open Bandits数据集上开展案例研究，为相关场景提供检测思路。
中文摘要 AI 辅助
在姑息治疗、信用分配和推荐系统等诸多领域，预测可能会对其所预测的结果产生因果影响，这种现象被称为结果述行性。本文提出一种名为结果述行性A/B检测（OPAB）的方法，用于检测结果述行性。OPAB通过评估不同预测组（干预措施）产生的结果分布的差异性来检测结果述行性，若该差异显著，则判定存在结果述行性。本文在不同的结果述行性假设类别下推导了OPAB的样本复杂度边界，并通过实验对其进行了验证。结果表明，在众多场景中使用OPAB检测结果述行性是可行的；同时还存在不可区分区域，即分配的干预措施数量不足以检测结果述行性的场景，这些结果对于样本稀缺、获取成本高昂或可能不道德的场景中结果述行性的可检测性具有更广泛的实际意义。本文最后在Open Bandits数据集上对OPAB的有效性进行了案例研究，并给出了未来的研究方向。
英文摘要
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.
发表机构
- Utrecht University（乌得勒支大学）
机构由 AI 辅助整理，请以论文原文为准。
