GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series
GRACE：高维时间序列因果边发现的门控精炼方法AI 解读 ↓
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GRACE：高维时间序列因果边发现的门控精炼方法
- 研究问题
- 高维时间序列中，非线性条件独立检验成本较高、连续边评分又依赖阈值时，如何兼顾候选边筛选效率与最终图的准确性？
- 方法与思路
- 作者先以快速线性条件独立检验生成候选骨架，再用带 Hard Concrete 门和 L0 正则化的模型，按预测贡献剔除候选边。
- 材料陈述的贡献
- 摘要称，模拟中该方法改善基础检验的 F1 并降低计算成本；时间 Bootstrap 变体在河流数据上减少大量骨架假阳性。
- 阅读关注点
- 应核对时间序列因果识别假设、门控预测目标与因果边的关系，以及自动正则化、基准比较和时间重抽样的具体实现。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要中的性能仅针对所述实验，未说明假设违背时仍有效识别因果的条件。
原文摘要
From climate teleconnections to gene regulation, modern time-series datasets encompass tens or hundreds of interacting variables, making causal discovery increasingly challenging. Constraint-based methods offer statistical rigor but their nonlinear CI tests are infeasible at scale, while score-based alternatives avoid CI testing but require arbitrary thresholds to binarize continuous edge scores. We propose GRACE ($\textbf{G}$ated $\textbf{R}$efinement for $\textbf{A}$ccurate $\textbf{C}$ausal $\textbf{E}$dge discovery), which refines constraint-based discovery using Hard Concrete gates with $L_0$ regularization: each candidate edge has an independent gate whose values concentrate near 0 or 1, yielding a clean bimodal separation that makes the binary decision robust, unlike the narrow, overlapping score distributions produced by $L_1$ and attention-based methods. A fast linear CI skeleton provides high-recall candidates; a single gated model then prunes false positives by learning which edges genuinely improve prediction, with automatic regularization adapted to problem dimensions and skeleton density. Systematic experiments on synthetic benchmarks, spanning diverse graph topologies (scale-free, Erdős-R'enyi, small-world) and dimensionalities up to $d=100$, show that GRACE substantially improves F1 over its base CI method while maintaining high precision, and outperforms attention-based and score-based alternatives. GRACE matches or exceeds expensive nonlinear CI tests at a fraction of the cost ($75\times$ faster). On a real-world river flow dataset, where rainfall confounders, variable propagation lags, and distributional shifts violate standard assumptions, a temporal bootstrap variant of GRACE recovers 9 of 11 causal edges along the Elbe River with only 1 false positive ($F_1 = 0.86$, AUROC${} = 0.99$), reducing the skeleton's 106 false positives by 99%.