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Source note2 min read

Implementing Causal Perception: Competing SCMs and Situated Fairness

Source

arXiv

arxiv.org/abs/2608.03917v1

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Plain-Language Brief

The paper presents the first implementation of a causal perception framework for multi-agent systems. This framework helps agents reason about a system and perceive its fairness when they have different interpretations of how the system functions (structural and parametrical causal perception). The study uses the German Credit dataset to illustrate how this perception affects accuracy and fairness in decision-making, showing that bias can be related to an agent's interpretation.

What the Document Says

  • Causal perception is about agents with competing Structural Causal Models (SCMs) inferring different probability distributions based on the same system and interventions.
  • This research applies the causal perception framework of Álvarez and Ruggieri (2025).
  • Structural causal perception occurs when agents disagree on the causal graph, and parametrical causal perception happens when they agree on the causal graph but disagree on its weights.
  • Algorithms are provided for computing interventional and counterfactual distributions.
  • Suitable distance measures are proposed to quantify disagreement between agents.
  • The study uses the German Credit dataset for illustration.
  • Results demonstrate that causal perception affects accuracy and fairness in a multi-expert decision setting.
  • The perception verdict is sensitive to the choice of distance metric and threshold.
  • Causal perception changes fairness assessments and threshold-based decisions.
  • Bias in this context is related to an agent's SCM, showing that competing worldviews in fairness problems cannot be ignored.

Key Concepts Mentioned

  • causal perception
  • Structural Causal Models (SCMs)
  • interventional distributions
  • counterfactual distributions
  • distance measures
  • German Credit dataset
  • multi-agent systems
  • accuracy
  • fairness
  • bias

Claims Made by the Document

  • Causal perception shapes how agents reason about a system and perceive its fairness.
  • Causal perception affects accuracy and fairness in a multi-expert decision setting.
  • Bias is situated with respect to the agent's SCM.

Evidence Used

  • The paper discusses operationalizing structural and parametrical causal perception.
  • Results from using the German Credit dataset are presented to demonstrate the effects of causal perception on accuracy and fairness.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.