Invariant Causal Routing for Governing Social Norms in Online Market Economies

TL;DR

This paper introduces Invariant Causal Routing (ICR), a causal framework that identifies stable policy-norm relations in online markets, enabling effective governance and norm stability across diverse environments.

Contribution

The paper proposes ICR, a novel causal governance method combining counterfactual reasoning and invariant discovery to promote stable social norms in online economies.

Findings

ICR produces more stable norms in simulations.

ICR achieves smaller generalization gaps.

ICR generates concise, interpretable policy rules.

Abstract

Social norms are stable behavioral patterns that emerge endogenously within economic systems through repeated interactions among agents. In online market economies, such norms -- like fair exposure, sustained participation, and balanced reinvestment -- are critical for long-term stability. We aim to understand the causal mechanisms driving these emergent norms and to design principled interventions that can steer them toward desired outcomes. This is challenging because norms arise from countless micro-level interactions that aggregate into macro-level regularities, making causal attribution and policy transferability difficult. To address this, we propose \textbf{Invariant Causal Routing (ICR)}, a causal governance framework that identifies policy-norm relations stable across heterogeneous environments. ICR integrates counterfactual reasoning with invariant causal discovery to separate genuine causal effects from spurious correlations and to construct interpretable, auditable policy rules that remain effective under distribution shift. In heterogeneous agent simulations calibrated with real data, ICR yields more stable norms, smaller generalization gaps, and more concise rules than correlation or coverage baselines, demonstrating that causal invariance offers a principled and interpretable foundation for governance.