Causal discovery in multivariate extremes is challenging because extreme observations are sparse, dependent, and often affected by latent common shocks. Existing approaches focus on undirected extremal dependence, require prior graph restriction, and do not scale beyond small systems. We introduce tail-induced asymmetry as a principle for causal directionality in heavy-tailed systems, where extreme events propagate asymmetrically so that forward tail prediction is systematically easier than backward prediction. We show that this asymmetry yields identifiable causal direction under a canonical max-linear model and provides a basis for score-based structure learning in the tail regime. Building on this, we propose Sparse Structure diScovery in Multivariate Extremes (S3ME), a two-stage data-driven framework for causal discovery. The first stage performs proxy-adjusted penalized neighbourhood selection to recover a sparse candidate skeleton under latent confounding. The second stage orients edges by minimizing tail prediction risk based on max-linear envelope models, exploiting directional asymmetry. We establish high-dimensional guarantees for skeleton screening and consistency of the score-based estimator under population separation conditions. Simulations demonstrate robustness to latent confounding and favourable scaling relative to existing extremal methods. Applications to river network data and financial tail-risk networks show that the approach recovers sparse, interpretable propagation structures without prespecified graph structure.

翻译：多元极端值中的因果发现极具挑战性，因为极端观测数据稀疏、具有依赖性，且常受潜在共同冲击影响。现有方法集中于无向极端依赖性，需预设图结构约束，且仅适用于小规模系统。我们提出尾部诱导非对称性作为重尾系统中因果方向性的新原理——极端事件以非对称方式传播，使得前向尾部预测系统性优于反向预测。研究表明，在典型最大线性模型下，该非对称性可产生可识别的因果方向，并为基于分数的尾部结构学习奠定基础。基于此，我们提出S3ME（稀疏结构多元极端值发现）两阶段数据驱动因果发现框架：第一阶段通过代理调整的惩罚邻域选择，在潜在混杂条件下恢复稀疏候选骨架；第二阶段利用最大线性包络模型，通过最小化尾部预测风险来定向边，从而利用方向非对称性。我们建立了高维条件下骨架筛选的理论保证，并证明了在总体分离条件下基于分数的估计量一致性。仿真实验表明该方法对潜在混杂具有鲁棒性，且与现有极端方法相比具有更优的可扩展性。河流网络数据与金融尾部风险网络的应用实例证实，该方法无需预设图结构即可恢复稀疏且可解释的传播结构。