We introduce ORACLE, a framework for explaining neural networks on tabular data and scientific factorial designs. ORACLE summarizes a trained network's prediction surface with main effects and pairwise interactions by treating the network as a black-box response, discretizing the inputs onto a grid, and fitting an orthogonal factorial (ANOVA-style) surrogate -- the $L^2$ orthogonal projection of the model response onto a finite-dimensional factorial subspace. A simple centering and $μ$-rebalancing step then expresses this surrogate as main- and interaction-effect tables that remain faithful to the original model in the $L^2$ sense. The resulting grid-based interaction maps are easy to visualize, comparable across backbones, and directly aligned with classical design-of-experiments practice. On synthetic factorial benchmarks and low- to medium-dimensional tabular regression tasks, ORACLE more accurately recovers ground-truth interaction structure and hotspots than Monte Carlo SHAP-family interaction methods, as measured by ranking, localization, and cross-backbone stability. We also discuss its scope in latent image and text settings: grid-based factorial surrogates are most effective when features admit an interpretable factorial structure, making ORACLE particularly well-suited to scientific and engineering workflows that require stable DoE-style interaction summaries.

翻译：本文提出ORACLE框架，用于解释神经网络在表格数据和科学析因设计中的行为。ORACLE通过将训练好的网络视为黑箱响应函数，将输入离散化到网格上，并拟合正交析因（方差分析风格）代理模型——即模型响应在有限维析因子空间上的$L^2$正交投影，从而以主效应和成对交互作用的形式概括网络的预测曲面。通过简单的中心化与$μ$重平衡步骤，该代理模型可表示为保持与原始模型$L^2$意义一致的主效应及交互效应表。所得基于网格的交互作用图谱易于可视化，在不同骨干网络间具有可比性，且与经典实验设计实践直接契合。在合成析因基准测试及中低维表格回归任务中，通过排序、定位和跨骨干网络稳定性等指标衡量，ORACLE比蒙特卡洛SHAP系列交互方法能更准确地还原真实交互结构与热点区域。本文还探讨了其在隐式图像与文本场景中的应用范围：当特征具有可解释的析因结构时，基于网格的析因代理模型最为有效，这使得ORACLE特别适用于需要稳定实验设计风格交互作用总结的科学与工程工作流。