Efficient Discovery of Approximate Causal Abstractions via Neural Mechanism Sparsification

TL;DR

This paper introduces a method to efficiently identify interpretable causal mechanisms in neural networks by using structured pruning to find high-level causal abstractions that are faithful under interventions.

Contribution

It reframes causal abstraction discovery as a structured pruning problem, deriving a new objective and criteria for extracting sparse, intervention-faithful causal mechanisms from trained networks.

Findings

The method recovers variance-based pruning as a special case.

It efficiently extracts intervention-faithful abstractions from pretrained networks.

Validation via interchange interventions confirms the effectiveness.

Abstract

Neural networks are hypothesized to implement interpretable causal mechanisms, yet verifying this requires finding a causal abstraction -- a simpler, high-level Structural Causal Model (SCM) faithful to the network under interventions. Discovering such abstractions is hard: it typically demands brute-force interchange interventions or retraining. We reframe the problem by viewing structured pruning as a search over approximate abstractions. Treating a trained network as a deterministic SCM, we derive an Interventional Risk objective whose second-order expansion yields closed-form criteria for replacing units with constants or folding them into neighbors. Under uniform curvature, our score reduces to activation variance, recovering variance-based pruning as a special case while clarifying when it fails. The resulting procedure efficiently extracts sparse, intervention-faithful abstractions from pretrained networks, which we validate via interchange interventions.