论文标题
在自相关的非线性时间序列数据集中发现同期和滞后的因果关系
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
论文作者
论文摘要
本文在因果关系充分的情况下，从观察时间序列中引入了一种基于线性和非线性，滞后和同时性因果发现的新型条件独立性（CI）方法。现有的基于CI的方法，例如PC算法以及其他框架的常见方法，其召回率低和部分膨胀的假阳性，以实现强大自相关，这是时间序列中无处不在的挑战。新颖的方法PCMCI $^+$扩展了PCMCI [Runge等，2019b]，包括发现同期链接。 PCMCI $^+$通过优化调理集的选择，甚至从自相关中受益，从而提高了CI测试的可靠性。该方法在Oracle案例中是独立的且一致的。广泛的数值实验表明，与其他方法相比，PCMCI $^+$具有较高的邻接检测能力，尤其是同时性方向回忆，同时更好地控制误报。优化的调理集也导致比PC算法要短得多的运行时间。 PCMCI $^+$在许多现实世界应用方案中都可以大量使用，因为时间分辨率通常太粗糙而无法解决时间延迟，并且存在强大的自相关。
The paper introduces a novel conditional independence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the causally sufficient case. Existing CI-based methods such as the PC algorithm and also common methods from other frameworks suffer from low recall and partially inflated false positives for strong autocorrelation which is an ubiquitous challenge in time series. The novel method, PCMCI$^+$, extends PCMCI [Runge et al., 2019b] to include discovery of contemporaneous links. PCMCI$^+$ improves the reliability of CI tests by optimizing the choice of conditioning sets and even benefits from autocorrelation. The method is order-independent and consistent in the oracle case. A broad range of numerical experiments demonstrates that PCMCI$^+$ has higher adjacency detection power and especially more contemporaneous orientation recall compared to other methods while better controlling false positives. Optimized conditioning sets also lead to much shorter runtimes than the PC algorithm. PCMCI$^+$ can be of considerable use in many real world application scenarios where often time resolutions are too coarse to resolve time delays and strong autocorrelation is present.
