CCW-Net 提出因果化概念解释框架，将中介分析引入神经网络可解释性
Causal Concept Explanations for Deep Neural Models
做自动驾驶或安全认证的同学可以看看，这篇把流行病学里的中介分析搬进了神经网络训练，解释不再是相关而是因果，还有必要性、充分性、独立性三条可量化准则，比传统概念解释方法严谨不少。
arXiv 论文提出 Causal Concept Wrapper Network（CCW-Net），首次将经济学、医学和流行病学中的干预式中介分析用作训练目标，让概念解释与模型输出之间的因果关系对齐。
此前的概念解释方法多为相关性分析，概念可以在既非必要也非充分的情况下看似正确实则误导。CCW-Net 通过反事实概念表征设定三条因果对齐准则：必要性将无关概念效应推向零，充分性确保相关概念表征能完全重构模型输出，独立性强制总效应在概念间线性分解。
论文还把中介分析作为评估工具，按概念标签组合量化任意模型输出中可归因于每个概念解释的因果份额。实验覆盖飞行器控制、驾驶和图像分类三类任务。
该工作面向自动驾驶等自主系统的认证与部署场景，使概念解释从可解释升级为可量化的因果解释。
Causal Concept Explanations for Deep Neural Models
Concept-based explanations in neural models are important for the development, certification, and adoption of autonomous systems. Yet, interpretability remains correlational. Without methods to guide and quantify causal relationships between explanations, a concept explanation can be provided without being necessary or sufficient for the observed output thereby appearing correct while misleading. We introduce Causal Concept Wrapper Network (CCW-Net), the first use of interventional mediation analysis from fields such as economics, medicine, and epidemiology as a training objective to align causal effects of concept-based explanations with a model's output. We further apply mediation analysis as an evaluation tool to quantify, per concept label combination, how much of any model's output is causally attributable to each concept explanation. As a training objective, CCW-Net shapes a model's concept representations such that they are both necessary and sufficient for the outputs they are credited with and independent of other concepts' contributions. Counterfactual concept representations anchor a set of causal alignment criteria: necessity, sufficiency, and independence. Necessity drives irrelevant concept effects toward zero, sufficiency ensures relevant concept representations fully reconstruct a model's output, and independence enforces the total effect decomposes linearly across concepts. We evaluate CCW-Net across aircraft control, driving, and image classification. CCW-Net provides a step toward concept-based explanations that are not merely interpretable, but quantifiably causal, supporting safer evaluation, certification, and deployment of transparent and trustworthy neural models.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include CCW-Net, 可解释性, 中介分析, 自动驾驶.
Source attribution: curated from arXiv cs.AI (@Josh Rountree, Pulkit Verma, Oswin So, Chuchu Fan, Julie A. Shah).