Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models
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超越系数：非线性时间序列模型中可解释因果发现的预测必要性检验
- 研究问题
- 非线性神经自回归模型中的因果得分应如何解释，得分相近的关系是否同样是准确预测所必需，而非仅具有较大参数值？
- 方法与思路
- 作者系统删除候选关系并比较预测表现，以神经加性向量自回归为案例，在涉及一百三十九国民主指标的时间序列数据中应用。
- 材料陈述的贡献
- 摘要称相似因果得分的关系可因冗余、时间持久性与机制差异而具有不同预测必要性，提出以预测比较辅助解释模型关系的框架。
- 阅读关注点
- 应核对删边后是否重新训练、预测比较的检验与验证方案，以及预测必要性如何与干预因果效应、隐藏混杂和跨国异质性区分。
- 阅读边界
- 仅据 arXiv 预印本摘要，不能确认同行评审或期刊接收；统计检验细节与因果识别条件未说明，预测必要性本身不保证干预因果性。
原文摘要
Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs remains poorly understood. In particular, causal scores produced by regularized neural autoregressive models are often treated as analogues of regression coefficients, leading to misleading claims of statistical significance. In this paper, we argue that causal relevance in nonlinear time-series models should be evaluated through forecast necessity rather than coefficient magnitude, and we present a practical evaluation procedure for doing so. We present an interpretable evaluation framework based on systematic edge ablation and forecast comparison, which tests whether a candidate causal relationship is required for accurate prediction. Using Neural Additive Vector Autoregression as a case study model, we apply this framework to a real-world case study of democratic development, modeled as a multivariate time series of panel data - democracy indicators across 139 countries. We show that relationships with similar causal scores can differ dramatically in their predictive necessity due to redundancy, temporal persistence, and regime-specific effects. Our results demonstrate how forecast-necessity testing supports more reliable causal reasoning in applied AI systems and provides practical guidance for interpreting nonlinear time-series models in high-stakes domains.