发表机构
Bloomberg L.P（彭博有限合伙企业）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对时间序列因果发现中的未观测混杂问题，提出LUCID自适应去混杂层，通过谱路由估计混杂机制并匹配去混杂策略，在合成分布外基准上显著提升图F1分数，且可包装现有发现算法。
AI 中文摘要
未观测到的共同原因在现实世界的时间序列中普遍存在，它们可能诱发虚假关联，使因果发现方法误将其识别为直接边。我们提出LUCID（混杂因素下的推断与发现学习），这是一种自适应机制的去混杂层，首先利用Marčenko-Pastur谱路由器从数据中估计混杂机制，然后应用与该机制匹配的去混杂策略。当谱指示普遍存在的因子混杂时，LUCID会衰减因子主导的变异，并从由此产生的创新中恢复同期（滞后0）结构，边选择则根据数据驱动的无边缘零分布进行校准。LUCID并不绑定于特定的发现算法，而是可以包装现有的发现引擎；我们在三种此类方法上展示了一致的改进。在一个涵盖混杂强度与稀疏性变化、载荷密度、滞后结构、波动性动态、边异质性、持续性、间歇性和尾部行为等多种变化的多样化合成分布外基准上，LUCID取得了最佳的族加权、定向、滞后分辨图F1分数（0.60），比最强基线提高了0.19的绝对值（约46%的相对提升）。其优势在更宽松的滞后折叠评分下进一步扩大，并在间歇性和重尾混杂下保持稳健。复现该方法、基准生成器及所有报告实验的代码可在以下https URL获取。
英文摘要
Unobserved common causes are pervasive in real-world time series and can induce spurious associations that causal discovery methods mistake for direct edges. We propose LUCID (Learning Under Confounding for Inference and Discovery, a regime-adaptive deconfounding layer that first estimates the confounding regime from data using a Marčenko--Pastur spectral router, then applies a deconfounding strategy matched to that regime. When the spectrum indicates pervasive factor confounding, LUCID attenuates factor-dominated variation and recovers contemporaneous (lag-$0$) structure from the resulting innovations, with edge selection calibrated against a data-driven edge-free null. Rather than being tied to a particular discovery algorithm, it can wrap existing discovery engines; we demonstrate consistent improvements across three such methods. On a diverse synthetic out-of-distribution benchmark spanning changes in confounder strength and sparsity, loading density, lag structure, volatility dynamics, edge heterogeneity, persistence, intermittency, and tail behavior, LUCID achieves the best family-weighted directed, lag-resolved graph $F_1$ ($0.60$), improving over the strongest baseline by $0.19$ absolute ($\approx\!46\%$ relative). Its advantage widens relative to looser lag-collapsed scoring, and remains robust under intermittent and heavy-tailed confounding. Code reproducing the method, the benchmark generators, and every reported experiment is available at https://github.com/bloomberg/causal-ts.
Comments18 pages, 2 figure
