Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO
基于 LASSO 可靠识别格兰杰因果关系的非渐近保证AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究稀疏自回归时间序列中的格兰杰因果识别、检验统计量与非渐近误差。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
基于 LASSO 可靠识别格兰杰因果关系的非渐近保证
- 研究问题
- 在时间序列长度有限且过程噪声相关时，如何用稀疏估计构造格兰杰因果检验，并刻画可靠识别所需的信息量？
- 方法与思路
- 作者在真实模型具有稀疏自回归表示的前提下构造 LASSO 统计量，分析非渐近性质，并提供两种数据驱动的阈值选择方法。
- 材料陈述的贡献
- 摘要称给出了可靠识别的基本限制、阈值规则的假阳性概率与检验功效刻画，并通过模拟和真实数据比较支持理论结果。
- 阅读关注点
- 应核对稀疏自回归与相关噪声条件、可靠识别的信号和样本要求，并区分格兰杰预测关系与结构性因果解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获核实；摘要未说明完整噪声条件及误差界，不能据此声称解决了任意混杂问题。
原文摘要
Granger causality is among the widely used data-driven approaches for causal analysis of time series data with applications in various areas including economics, molecular biology, and neuroscience. Two of the main challenges of this methodology are: 1) over-fitting as a result of limited data duration, and 2) correlated process noise as a confounding factor, both leading to errors in identifying the causal influences. Sparse estimation via the LASSO has successfully addressed these challenges for parameter estimation. However, the classical statistical tests for Granger causality resort to asymptotic analysis of ordinary least squares, which require long data duration to be useful and are not immune to confounding effects. In this work, we address this disconnect by introducing a LASSO-based statistic and studying its non-asymptotic properties under the assumption that the true models admit sparse autoregressive representations. We establish fundamental limits for reliable identification of Granger causal influences using the proposed LASSO-based statistic. We further characterize the false positive error probability and test power of a simple thresholding rule for identifying Granger causal effects and provide two methods to set the threshold in a data-driven fashion. We present simulation studies and application to real data to compare the performance of our proposed method to ordinary least squares and existing LASSO-based methods in detecting Granger causal influences, which corroborate our theoretical results.