Abstract
Explainable Artificial Intelligence (XAI) is increasingly required in computational economics, where machine-learning forecasters can outperform classical econometric models but remain difficult to audit and use for policy. This survey reviews and organizes the growing literature on XAI for economic time series, where autocorrelation, non-stationarity, seasonality, mixed frequencies, and regime shifts can make standard explanation techniques unreliable or economically implausible. We propose a taxonomy that classifies methods by (i) explanation mechanism: propagation-based approaches (e.g., Integrated Gradients, Layer-wise Relevance Propagation), perturbation and game-theoretic attribution (e.g., permutation importance, LIME, SHAP), and function-based global tools (e.g., Accumulated Local Effects); (ii) time-series compatibility, including preservation of temporal dependence, stability over time, and respect for data-generating constraints. We synthesize time-series-specific adaptations such as vector- and window-based formulations (e.g., Vector SHAP, WindowSHAP) that reduce lag fragmentation and computational cost while improving interpretability. We also connect explainability to causal inference and policy analysis through interventional attributions (Causal Shapley values) and constrained counterfactual reasoning. Finally, we discuss intrinsically interpretable architectures (notably attention-based transformers) and provide guidance for decision-grade applications such as nowcasting, stress testing, and regime monitoring, emphasizing attribution uncertainty and explanation dynamics as indicators of structural change.
Abstract (translated)
可解释的人工智能（XAI）在计算经济学中日益受到重视，因为机器学习预测模型可以优于传统计量经济模型，但它们难以审核和用于政策制定。本文综述并整理了关于时间序列经济数据的XAI不断增长的相关文献。在这种背景下，自相关性、非平稳性、季节性变化、混合频率以及制度转换等因素使得标准解释技术变得不可靠或缺乏经济合理性。 我们提出了一种分类方法，根据（i）解释机制：基于传播的方法（例如集成梯度法和逐层相关传播），基于干扰的游戏理论归因方法（如排列重要性、LIME 和 SHAP），以及基于函数的全局工具；（ii）时间序列兼容性，包括保持时间依赖关系、时间稳定性以及尊重数据生成约束。我们综合了针对时间序列具体适应性的改进，例如向量和窗口基形式（如Vector SHAP 和 WindowSHAP），这些方法减少了滞后碎片化，并降低了计算成本，同时提高了可解释性。 此外，本文还探讨了解释性和因果推断及政策分析之间的联系，通过干预归因（因果 Shapley 值）和受限反事实推理进行连接。最后，我们讨论了本原具有解释性的架构（特别是基于注意力的转换器），并为决策级应用如即时预测、压力测试和制度监控提供了指导，并强调了归因不确定性以及解释动态性作为结构变化的指标。 这一综述旨在帮助经济学研究者更好地理解如何利用XAI技术来增强机器学习模型在时间序列分析中的透明度与可靠性，从而促进更有效的政策制定。
URL
https://arxiv.org/abs/2512.12506
