Identification and Inference for Causal Effects in Extremes under General Conditions
一般条件下极端事件因果效应的识别与推断AI 解读 ↓
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一般条件下极端事件因果效应的识别与推断
- 研究问题
- 当变量尾指数不同且可能存在重尾混杂时，如何识别极端事件之间的因果方向，并区分直接作用与混杂造成的极端依赖？
- 方法与思路
- 作者在线性结构因果模型和正则变化创新下分析因果尾部系数的极限，利用代理信息调整该系数，并构造估计和检验程序。
- 材料陈述的贡献
- 摘要称刻画异质尾部和混杂对识别的影响，指出足够重尾的混杂可与直接因果效应不可区分，并在适当代理信息下恢复识别。
- 阅读关注点
- 应核对尾指数关系、结构模型和代理信息要求，以及尾部阈值选择、估计渐近条件和因果方向与重尾混杂检验的适用范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；一般条件仍限于摘要所述模型，尾部因果系数也不能直接解释为干预效应大小。
原文摘要
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.