Develop efficient and accurate causal attribution methods

Develop attribution methods for deep neural networks that efficiently and accurately measure the causal importance of inputs or upstream components on downstream activations and predictions, overcoming limitations of first‑order gradient approximations and distribution‑shifting perturbations.

Background

Attribution techniques aim to quantify the causal contribution of inputs or internal components to outputs. Many current approaches rely on gradients (first‑order approximations) or perturbations that may take models off-distribution, each with known theoretical and practical pitfalls.

Given these limitations and adversarial vulnerabilities, the authors conclude that current methods are insufficiently reliable or efficient. A robust solution would be broadly useful for interpretation, validation, and downstream applications such as debugging and safety auditing.