AI 中文总结
本文针对一般条件下的极端事件因果效应，提出基于因果尾系数的估计与推断方法，解决重尾混杂等问题，可识别平均依赖方法无法检测的因果关系。
AI 中文摘要
理解极端事件的传导在众多经济与环境应用中至关重要，但大多数用于因果推断的计量经济学方法聚焦于平均效应而非尾部行为。本文研究极端事件中因果关系的识别问题，并推导相应的估计量及其渐近推断。作为变量极端实现值之间因果依赖的度量，我们在具有重尾正则变化创新的线性结构因果模型中分析因果尾系数（Causal Tail Coefficient, CTC）的渐近行为。与现有文献不同，我们允许系统中的变量呈现异质尾指数，并考虑潜在重尾混杂因素的存在。我们推导了在这些条件下评估CTC极限行为的理论结果，表明尾行为的差异如何有助于实现因果结构的识别。轻尾混杂因素在渐近意义上可忽略，但足够重尾的混杂因素可诱导出与直接因果效应观测上无法区分的极端依赖模式。当存在合适的代理信息时，可使用调整后的因果尾系数恢复识别。基于这些结果，我们开发了一般条件下极端事件因果关系的估计与推断程序。我们建立了所提估计量的渐近性质，并推导了因果方向与重尾混杂的检验。模拟证据考察了它们的有限样本性能，并为其实施提供指导。对气候与金融极端事件的应用表明，所提方法可揭示针对平均依赖的方法可能无法检测到的因果关系。
英文摘要
Understanding the propagation of extreme events is important in many economic and environmental applications, yet most econometric methods for causal inference focus on average effects rather than tail behavior. This paper studies the identification of causal relations in extremes and derives resulting estimators and their asymptotic inference. As measure of causal dependence between extreme realizations of variables, we analyze the asymptotic behavior of the Causal Tail Coefficient (CTC) within a linear structural causal model with heavy-tailed regularly varying innovations. In contrast to the existing literature, we allow the variables in the system to exhibit heterogeneous tail indices and consider the presence of potentially heavy-tailed confounders. We derive theoretical results assessing the limiting behavior of the CTC under these conditions and show how differences in tail behavior can help to reach identification of the causal structure. Light-tailed confounders are asymptotically negligible, but sufficiently heavy-tailed confounders can induce extremal dependence patterns that are observationally indistinguishable from direct causal effects. When suitable proxy information is available, identification can be recovered using an adjusted Causal Tail Coefficient. Based on these results, we develop estimation and inference procedures for causal relations in extremes under general conditions. We establish asymptotic properties of the proposed estimators and derive tests for the causal direction and heavy-tailed confounding. Simulation evidence examines their finite-sample performance and provides guidance on their implementation. Applications to climate and financial extremes illustrate how the proposed methods can uncover causal relations that may remain undetected by approaches targeting average dependence.
Comments51 pages, 20 figures
