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 p...
| Authors | Jose M. Álvarez |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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