ArXiv

Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs

2026-08-04 · 14:57 UTC ·Jannick Strobel, Muqsit Azeem, Stefan Leue ·1 min read
Authors
Jannick Strobel, Muqsit Azeem, Stefan Leue
Categories
cs.AI, cs.LG, cs.LO
arXiv
https://arxiv.org/abs/2608.03772v1
PDF
https://arxiv.org/pdf/2608.03772v1

Brief

The paper formalizes explanations for neural-network predictions as Halpern–Pearl actual causes under Boolean structural causal models to account for input dependencies. It uses bound propagation plus branch-and-bound to compute all minimal HP causes with formal completeness and minimality guarantees, scaling to search spaces up to 2.3×10^13 and 28-node SCMs within 180 s and outperforming brute-force/ILP and heuristic baselines; a case study finds 14.9% spurious causes if dependencies are ignored. (Based on the abstract; full text not available.)

Why it matters

Formalized neural-network explanations as Halpern–Pearl (HP) actual causes using Boolean Structural Causal Models (SCMs); computes HP causes via bound propagation and branch-and-bound and provides formal guarantees of completeness and minimality.

Key details

  • Empirically scales to search spaces of up to 2.3×10^13 candidate (cause, contingency) pairs on SCMs with up to 28 nodes, solving instances within a 180 s per-instance budget and outperforming brute-force and ILP baselines and heuristic search as graph size grows.
  • Case study shows that treating inputs as independent inflates reported causes: 14.9% of causes reported when ignoring dependencies are spurious under the authors' SCM.
Source evidence

Abstract

Explaining the predictions of neural networks is a central challenge in trustworthy AI. Existing explanation methods, such as those based on feature attribution or minimal sufficient sets, typically treat input features as independent, which can yield misleading explanations when inputs exhibit structured dependencies. We address this by formalizing explanations as Halpern-Pearl (HP) actual causes, modeling input dependencies using Boolean Structural Causal Models (SCMs). We compute HP causes by applying bound propagation and branch-and-bound techniques, while providing formal guarantees of completeness and minimality. Our experiments show that we substantially outperform brute-force and ILP baselines in scalability, and outperform heuristic search as graph size grows, computing all minimal actual causes on instances with search spaces of up to $2.3\times10^{13}$ candidate (cause, contingency) pairs, on SCMs with up to 28 nodes, within a 180s per-instance budget. In a case study, we further show that ignoring input dependencies inflates the number of reported causes, 14.9% of which are spurious under our SCM.