Influence estimators disagree by design, not just approximation error
A new paper formalizes a critical gap in how researchers measure training data influence on model outputs. Rather than treating disagreement between influence estimators as mere approximation error, the work identifies specification mismatch as the root cause: different choices about what behavior to attribute, how to intervene on training data, and what counterfactual training process to simulate produce fundamentally incompatible rankings. This matters for practitioners building data valuation and debugging pipelines, since selecting the wrong specification can yield misleading conclusions about which examples matter most. The framing shifts influence estimation from a technical approximation problem into a conceptual one, forcing teams to be explicit about their causal assumptions before comparing methods.
Modelwire context
ExplainerThe paper doesn't just say influence estimators disagree; it argues that disagreement is often not a bug to fix but a signal that researchers are answering different causal questions. This reframes the entire field from 'which method approximates ground truth best' to 'which causal model matches your use case.'
This connects directly to the PIA work from earlier today, which also separated concerns around domain-specific semantics in high-stakes contexts. Just as PIA argued that clinical records can't be flattened into generic text embeddings without losing precision, this paper argues that influence rankings can't be compared across specifications without first aligning on what causal intervention you're actually modeling. Both papers reject the one-size-fits-all framing and force practitioners to be explicit about their assumptions before choosing tools.
If major data valuation frameworks (Cleanlab, Snorkel) release updated documentation within the next six months that explicitly surfaces counterfactual specification choices to users, that signals the field is absorbing this lesson. If they don't, practitioners will likely keep treating influence estimates as interchangeable, defeating the paper's core contribution.
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Which Influence Are We Estimating? The Role of Counterfactual Specifications in Data Attribution”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.