While modern multivariate forecasters such as Transformers and GNNs achieve strong benchmark performance, they often suffer from systematic errors at specific variables or horizons and, critically, lack guarantees against performance degradation in deployment. Existing post-hoc residual correction methods attempt to fix these errors, but are inherently greedy: although they may improve average accuracy, they can also "help in the wrong way" by overcorrecting reliable predictions and causing local failures in unseen scenarios. To address this critical "safety gap," we propose CRC (Causality-inspired Safe Residual Correction), a plug-and-play framework explicitly designed to ensure non-degradation. CRC follows a divide-and-conquer philosophy: it employs a causality-inspired encoder to expose direction-aware structure by decoupling self- and cross-variable dynamics, and a hybrid corrector to model residual errors. Crucially, the correction process is governed by a strict four-fold safety mechanism that prevents harmful updates. Experiments across multiple datasets and forecasting backbones show that CRC consistently improves accuracy, while an in-depth ablation study confirms that its core safety mechanisms ensure exceptionally high non-degradation rates (NDR), making CRC a correction framework suited for safe and reliable deployment.

翻译：尽管现代多元预测模型（如Transformer和图神经网络）在基准测试中表现出色，但它们常在特定变量或预测步长上存在系统性误差，且关键的是，在部署时缺乏性能不下降的保证。现有的事后残差校正方法试图修正这些误差，但其本质是贪婪的：虽然可能提升平均精度，但也可能“以错误的方式提供帮助”——过度校正可靠的预测，并在未见场景中引发局部失效。为填补这一关键的“安全鸿沟”，我们提出CRC（因果启发的安全残差校正），一种即插即用的框架，其明确设计旨在确保性能不下降。CRC遵循分而治之的理念：它采用因果启发的编码器，通过解耦自身变量与跨变量动态来揭示方向感知的结构；并采用混合校正器来建模残差误差。关键的是，校正过程受严格四重安全机制调控，以防止有害的更新。在多个数据集和预测主干模型上的实验表明，CRC能持续提升精度，而深入的消融研究证实，其核心安全机制确保了极高的非退化率，使得CRC成为一个适用于安全可靠部署的校正框架。