Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims

2026-05-08 · 17:01 UTC ·Zezheng Lin, Fengming Liu ·1 min read

Brief

Lin and Liu show that mechanistic interpretability work often uses causal vocabulary (circuits, mediators, causal abstraction) while omitting explicit identification assumptions. Via a purposive audit of 10 papers and a two-coder check on 30 items, they document widespread substitution of validation metrics for identification and offer a five-step disclosure norm to make causal claims explicit. Submitted to NeurIPS 2026 (Position Track).

Abstract

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 support without stating the assumptions that make them identifying. A two-human-coder audit on $n=30$ reproduces the direction of the main finding: dedicated identification sections are absent, and validation-metric substitution is common, though exact Dim B/D counts are coding-rule sensitive. The paper proposes a disclosure norm: state whether the claim is causal, name the identification strategy, enumerate assumptions, stress at least one, and explain how conclusions shift if assumptions fail. Validation is not identification.

Comment: 10 pages, 2 figures. Submitted to NeurIPS 2026 (Position Track)