论文标题
多阶段因果结构学习
Multiscale Causal Structure Learning
论文作者
论文摘要
观察到的数据的因果结构的推论在揭示系统的基本动力学方面起着关键作用。本文揭示了一种新的方法，称为多阶段 - 伴侣结构学习（MS-Castle），以估计在不同时间尺度上发生的线性因果关系的结构。与现有方法不同的是，MS-Castle明确考虑了多个时间序列之间的即时和滞后相互关系，以不同的尺度表示，呈现固定小波变换和非凸线优化。 MS-Castle作为特殊情况将单个版本纳入了名为SS-Castle的单尺度版本，该版本在计算效率，性能和鲁棒性方面相对于合成数据而言，它在计算效率，性能和鲁棒性方面进行了比较。我们使用MS-Castle研究了Covid-19大流行期间15个全球股票市场风险的多尺度因果结构，这说明了MS-Castle的多尺度分析，胜过SS-Castle，MS-Castle如何提取有意义的信息。我们发现，最持久和最强烈的互动发生在中期决议。此外，我们确定了在经过考虑的时期内推动风险的股票市场：巴西，加拿大和意大利。拟议的方法可以由金融投资者利用，这些投资者取决于其投资视野，可以从因果关系的角度管理股票投资组合中的风险。
The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causal Structure Learning (MS-CASTLE), to estimate the structure of linear causal relationships occurring at different time scales. Differently from existing approaches, MS-CASTLE takes explicitly into account instantaneous and lagged inter-relations between multiple time series, represented at different scales, hinging on stationary wavelet transform and non-convex optimization. MS-CASTLE incorporates, as a special case, a single-scale version named SS-CASTLE, which compares favorably in terms of computational efficiency, performance and robustness with respect to the state of the art onto synthetic data. We used MS-CASTLE to study the multiscale causal structure of the risk of 15 global equity markets, during covid-19 pandemic, illustrating how MS-CASTLE can extract meaningful information thanks to its multiscale analysis, outperforming SS-CASTLE. We found that the most persistent and strongest interactions occur at mid-term time resolutions. Moreover, we identified the stock markets that drive the risk during the considered period: Brazil, Canada and Italy. The proposed approach can be exploited by financial investors who, depending to their investment horizon, can manage the risk within equity portfolios from a causal perspective.
