AI RESEARCH

Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims

arXiv CS.AI

ArXi:2605.08012v1 Announce Type: cross Mechanistic interpretability papers increasingly use causal vocabulary: circuits, mediators, causal abstraction, monosemanticity. Such claims require explicit identification assumptions. A purposive audit of 10 papers across four methodological strands finds no dedicated identification-assumptions section and a recurring pattern: validation metrics such as faithfulness, completeness, monosemanticity, alignment, or ablation effects are reported as causal without stating the assumptions that make them identifying.