Bloomberg 提出 LUCID：从含隐混杂因素的时间序列中做因果发现
LUCID: Learning Under Confounding for Inference and Discovery in Time Series
Bloomberg 开源了 LUCID，专治时间序列里隐变量混杂导致的假因果边，F1 比最强基线高出 46%，做因果推断的可以试试。
Bloomberg 团队发布因果发现方法 LUCID（Learning Under Confounding for Inference and Discovery）。该方法用 Marčenko--Pastur 谱路由先从数据中估计混杂机制，再匹配相应的去混杂策略，可套用在三种现有因果发现算法上。在覆盖混杂强度、稀疏度、波动动态等变化的多项合成分布外基准中，LUCID 取得家族加权有向滞后图 F1 得分 0.60，比最强基线绝对提升 0.19，相对提升约 46%。代码与基准生成器已在 GitHub 开源。
LUCID: Learning Under Confounding for Inference and Discovery in Time Series
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.