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arXiv · 2509.10825

CUBE: Contrastive Understanding by Balanced Experiments

Dongseok Kim·Hyoungsun Choi·Mohamed Jismy Aashik Rasool·Gisung Oh
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Abstract

Post-hoc explanation depends on how model queries are organized. We propose CUBE, a design-based framework that explains a trained predictive model through balanced low--high probes. Selected variables define factors, designed feature-level combinations define query conditions, and model predictions are summarized as factorial contrasts. CUBE reports main effects and pairwise interactions as controlled readings of average and conditional response changes over a declared design space. Experiments on synthetic and real tabular tasks show that CUBE recovers dominant learned effect structure, clarifies query-efficient identifiability, and supports screening--follow-up refinement.

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Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool, Gisung Oh. 2025-09-13. CUBE: Contrastive Understanding by Balanced Experiments. https://arxiv.org/abs/2509.10825

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