From Observation to Intervention: A Causal Audit of Expert Importance in Mixture-of-Experts Models
arXiv:2606.10703v1 Announce Type: cross Abstract: Interpretability methods routinely use population-level summary statistics over observed model behaviour to license claims about the effects of targeted interventions on specific computations; in Pearl's terms, they treat rung-1 associational evidence as if it supported rung-2 interventional conclusions, a move whose validity is rarely tested. We examine one concrete instance: the use of routing statistics in Mixture-of-Experts (MoE) pruning, where utilization rates, activation norms, and routing weight distributions are treated as predictors o
The increasing sophistication and scale of AI models, particularly Mixture-of-Experts architectures, is driving a critical need for deeper interpretability and robust evaluative methods beyond mere associational statistics.
This research provides a foundational theoretical toolkit to rigorously evaluate and compare different interpretability methods for complex AI models by focusing on causal intervention rather than correlation.
Interpretability methods for AI models will shift towards more causally sound approaches, potentially leading to more reliable model audits, safer deployments, and more effective model optimization strategies.
- · AI safety researchers
- · AI developers
- · Auditors and regulators
- · High-stakes AI applications
- · Developers relying solely on associational interpretability metrics
- · Companies with opaque AI systems
- · Unreliable AI interpretability startups
Improved understanding and trustworthiness of complex AI models, especially MoE architectures.
Faster and more reliable iteration cycles for AI model development and pruning as interpretability becomes more actionable.
Enhanced AI explainability fostering greater public and regulatory trust, potentially accelerating AI adoption in sensitive domains.
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