实现因果感知：竞争结构因果模型与情境公平性
Implementing Causal Perception: Competing SCMs and Situated Fairness
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中文总结 AI 辅助
本研究首次实现Álvarez与Ruggieri（2025）的因果感知框架，通过算法量化竞争SCM的分歧，结合德国信用数据集验证其对多智能体决策公平性的影响，凸显公平问题中竞争世界观的重要性。
中文摘要 AI 辅助
因果感知是指对同一系统拥有竞争结构因果模型（SCM）的智能体，在相同干预集下推断出不同概率分布（包括各智能体SCM隐含的假设分布）的现象。它影响智能体对系统的推理方式及对公平性的感知。因果感知是一种有前景的概率框架，但此前仅为理论层面。本研究首次实现了Álvarez与Ruggieri（2025）提出的因果感知框架，将结构型（智能体对因果图存在分歧）和参数型（智能体对因果图达成共识但权重存在分歧）因果感知进行可操作化；设计了计算干预分布与反事实分布的算法，提出合适的距离度量以量化分歧。利用德国信用（German Credit）数据集，本研究阐明了因果感知如何影响多智能体决策场景下的准确率与公平性，结果表明感知判断对距离度量和阈值的选择敏感，且因果感知会改变公平性评估及基于阈值的决策，偏见相对于智能体的SCM具有情境性，证明公平性问题中的竞争世界观不可忽视。
英文摘要
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.
