不同专家因因果模型差异，对同一系统公平性判断不同。
Implementing Causal Perception: Competing SCMs and Situated Fairness
- 用结构与参数差异建模多方因果认知冲突
- 在德国信贷数据上验证认知差异影响决策准确率与公平性
- 揭示公平性是依赖于具体因果模型的相对概念
当拥有竞争性结构因果模型（SCM）的智能体对同一系统推断出不同概率分布时，即发生因果感知，这包括在相同干预下各自模型所隐含的假设分布。因果感知塑造了智能体对系统的推理方式及其公平性认知。尽管这一框架具有潜力，但长期停留在理论阶段。本文首次实现了Álvarez和Ruggieri（2025）提出的因果感知框架，操作化了结构性（因果图不一致）与参数性（因果图一致但权重不同）的因果感知。设计算法计算干预与反事实分布，并提出合适的距离度量以量化分歧。基于德国信贷数据，我们展示了因果感知如何影响多专家决策中的准确性和公平性。结果表明，感知结论对距离度量和阈值选择敏感；同时，因果感知会改变公平性评估与阈值决策。偏差具有情境依赖性，凸显了在公平性问题中多元世界观不可忽视。
原文摘要 · Abstract (English)
Causal perception occurs when agents with competing Structural Causal Models (SCMs) of the same system infer different probability distributions, including the hypothetical distributions implied by each agent's SCM under the same set of interventions. It shapes how agents reason about the system and how they perceive its fairness. Causal perception is a promising probabilistic framework, but it has remained purely theoretical. This work provides the first implementation of the causal perception framework of Álvarez and Ruggieri (2025). We operationalize structural (agents disagree on the causal graph) and parametrical (agents agree on the causal graph but disagree on its weights) causal perception. We design algorithms for computing interventional and counterfactual distributions and propose suitable distance measures to quantify the disagreement. Using the German Credit dataset, we illustrate how causal perception affects accuracy and fairness in a multi-expert decision setting. We show that the perception verdict is sensitive to the choice of distance metric and threshold. We also show that causal perception changes fairness assessments and threshold-based decisions. Bias proves situated with respect to the agent's SCM, demonstrating that competing worldviews in fairness problems cannot be ignored.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。