一作ICML 2026Poster· General Machine Learning / Causality
Position: Causality is Key for Interpretability Claims to Generalise
Shruti Joshi · Aaron Mueller · David Klindt · Wieland Brendel · Dhanya Sridhar · Patrik Reizinger
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Shruti Joshi · Aaron Mueller · David Klindt · Wieland Brendel · Dhanya Sridhar · Patrik Reizinger
Aaron Mueller · Andrew Lee · Shruti Joshi · Ekdeep Singh Lubana · Dhanya Sridhar · Patrik Reizinger
A goal of interpretability is to recover disentangled representations of latent concepts (features) from the activations of neural networks. The quality of features is typically evaluated in isolation, and under implicit independence assumptions that may not hold in practice. Thus, it is unclear...