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Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims

arXiv 2605.08012 ↗First posted 8 May 2026cs.LG cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 8 May 2026.

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9 of 22 references.

References · 9

  1. 2022Emergent Abilities of Large Language Modelscited by 2,483
  2. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,130
  3. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  4. 2023Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representationscited by 197
  5. 2024Transcoders Find Interpretable LLM Feature Circuitscited by 186
  6. 2023Interpretability at Scale: Identifying Causal Mechanisms in Alpacacited by 102
  7. 2025Sparse Autoencoders Do Not Find Canonical Units of Analysiscited by 80
  8. 2023Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patchingcited by 75
  9. 2023Rigorously Assessing Natural Language Explanations of Neuronscited by 44

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