这篇论文把因果感知从理论变成了能跑的算法，在德国信用数据集上验证了不同模型对公平性判断的影响，值得搞公平性研究的人看看。
该论文首次实现了 Álvarez 和 Ruggieri (2025) 提出的因果感知概率框架。论文将因果感知区分为结构性和参数性两类，并设计了基于 SCM 的算法来计算干预分布与反事实分布。在 German Credit 数据集上的多专家决策实验中，因果感知改变了公平性评估和基于阈值的决策。结果显示，感知判定对距离度量和阈值的选择敏感，偏见相对于智能体的 SCM 具有情境性。
Implementing Causal Perception: Competing SCMs and Situated Fairness
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.