  Automated interpretability research has recently attracted attention as a
potential research direction that could scale explanations of neural network
behavior to large models. Existing automated circuit discovery work applies
activation patching to identify subnetworks responsible for solving specific
tasks (circuits). In this work, we show that a simple method based on
attribution patching outperforms all existing methods while requiring just two
forward passes and a backward pass. We apply a linear approximation to
activation patching to estimate the importance of each edge in the
computational subgraph. Using this approximation, we prune the least important
edges of the network. We survey the performance and limitations of this method,
finding that averaged over all tasks our method has greater AUC from circuit
recovery than other methods.
