通过自适应干预，快速检测线性因果模型中的变化点。
Quickest Causal Change Point Detection by Adaptive Intervention
- 用中心化技术将因果传播的变化集中到单一维度。
- 基于KL散度选干预节点，放大变化幅度，提升检测效率。
- 适合需要实时监测因果结构变化的系统，如金融或医疗监控。
我们提出一种针对线性因果模型中变化点监测的算法，可处理干预影响。通过一种特殊的中心化技术，可将由因果传播引起的节点间变化集中到单一维度。此外，基于Kullback-Leibler散度选择合适的干预节点，可放大变化幅度。我们还提出了一个干预值选择算法，以识别最有效的干预节点。两种监测方法均采用自适应干预策略，在探索与利用之间取得平衡。理论上证明了所提方法的一阶最优性，并通过模拟数据集及两个真实世界案例研究验证了其有效性。
原文摘要 · Abstract (English)
We propose an algorithm for change point monitoring in linear causal models that accounts for interventions. Through a special centralization technique, we can concentrate the changes arising from causal propagation across nodes into a single dimension. Additionally, by selecting appropriate intervention nodes based on Kullback-Leibler divergence, we can amplify the change magnitude. We also present an algorithm for selecting the intervention values, which aids in the identification of the most effective intervention nodes. Two monitoring methods are proposed, each with an adaptive intervention policy to make a balance between exploration and exploitation. We theoretically demonstrate the first-order optimality of the proposed methods and validate their properties using simulation datasets and two real-world case studies.
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