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quote:causal-abstraction-implicitly-relies-on-strong-assumptions-about-how-features-are-encoded-in-deep-neural-networks-dnns-and-becomes-trivial-without-such-assumptions

causal abstraction implicitly relies on strong assumptions about how features are encoded in deep neural networks (DNNs), and becomes trivial without such assumptions

Load-bearing formulation of the paper's central argument

Source paper

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The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability?

(2025) · Sutter, Denis · Minder, Julian · Hofmann, Thomas · Pimentel, Tiago

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Causal abstraction is not enough for mechanistic interpretability because it becomes vacuous without assumptions about how models encode information
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Central thesis of the paper

Related by similarity (8)

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Causal abstraction implicitly relies on strong assumptions about feature encoding in DNNs, and becomes trivial without such assumptionsclaim0.944
Authors' interpretation connecting their proof to practical interpretability methodology
What can causal abstraction analyses tell us about how DNNs encode features if the methods themselves rely on encoding assumptions?question0.897
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An interplay between causal abstraction and feature geometry deepens mechanistic understanding of language modelsclaim0.818
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Early causal abstraction methods (Geiger et al. 2021) implicitly rely on the privileged bases hypothesis, while recent methods (Geiger et al. 2024b) rely on the linear representation hypothesisclaim0.809
Historical framing of how representation assumptions have evolved in causal interpretability
What is the connection between information encoding assumptions and causal abstraction?question0.804
Identified as exciting future work direction
Investigating the causal substructure of neural representations is necessary to avoid misidentifying data structures of simpler representations as abstract conceptsclaim0.790
Motivated by the finding that lexical entailment decomposes into word identities.
DAS overcomes the localist limitation of prior causal abstraction by allowing individual neurons to play multiple roles via non-standard basesclaim0.779
Central claim motivating DAS over prior methods.
Ultimately, we would like to understand neural networks well enough to be able to intentionally design them.quote0.775
Vision statement in the conclusion.