面向非线性不确定系统因果发现的多项式混沌框架
A Polynomial Chaos Framework for Causal Discovery in Nonlinear Uncertain Systems
该框架将多项式混沌展开（PCE）嵌入结构方程以建模任意噪声分布，为视觉任务中非线性非高斯噪声下的因果发现提供了可识别性保证和不确定性量化手段，可迁移至视觉可解释性分析或鲁棒特征选择。
原文依据 we introduce a novel causal discovery framework that integrates Polynomial Chaos Expansion (PCE) representations of stochastic noise into structural equations.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在安全关键型工业应用中，准确识别因果关系并量化不确定性对于根因分析、特征选择和过程优化等任务至关重要。传统因果发现方法难以充分处理工业传感器数据中普遍存在的非线性和复杂不确定性。为此，我们提出了一种新颖的因果发现框架，将随机噪声的多项式混沌展开（PCE）表示集成到结构方程中。该方法有效捕捉了工业数据特有的复杂非线性耦合和任意噪声分布。我们在混沌系数满足温和稀疏性条件下严格证明了因果结构的可识别性，扩展了线性非高斯无环模型（LiNGAM）的可识别性结果。大量实验表明，该方法具有更高的准确性、在非高斯噪声条件下的鲁棒性以及实用的不确定性量化能力。该框架为非线性不确定工业环境中的因果分析提供了一种有原则、可解释且计算可行的途径。
英文原文摘要 1152 字符
In safety-critical industrial applications, accurately identifying causal relationships and quantifying uncertainty is essential for tasks such as root cause analysis, feature selection, and process optimization. Traditional causal discovery methods inadequately handle nonlinearities and complex uncertainties prevalent in industrial sensor data. To address this, we introduce a novel causal discovery framework that integrates Polynomial Chaos Expansion (PCE) representations of stochastic noise into structural equations. This method effectively captures complex nonlinear couplings and arbitrary noise distributions characteristic of industrial data. We rigorously prove the identifiability of causal structures under mild sparsity conditions on the chaos coefficients, extending linear non-Gaussian acyclic model (LiNGAM) identifiability results. Extensive experiments demonstrate superior accuracy, robustness under non-Gaussian noise conditions, and practical uncertainty quantification. This framework presents a principled, interpretable, and computationally feasible approach to causal analysis in nonlinear uncertain industrial environments.