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.
翻译:本文介绍了一种基于线性和非线性、滞后和同时期因果发现的新有条件独立(CI)法,该方法来自因果充足情况下的观察时间序列;基于CI的现有方法,如PC算法和其他框架的共同方法,其回调率低,且部分夸大了强烈的自动通缩的假正数,这是时间序列中普遍存在的挑战;新方法PCMCI$$,将PCMICI[Runge等人,2019b]扩展至包括发现同时线链接。PCMCI$ 提高CI测试的可靠性,优化调控机组的选择,甚至从自动通缩中获益。这种方法在Ocle案件中是独立和一致的。一系列广泛的数字实验表明,PCMCI$具有较高的相近检测能力,特别是同时方向比其他方法更能控制。优化的调控器也比PC算法的运行时间要短得多。PCCCCCI$ 美元在许多实际应用情景中可以大量使用,在这些情景中,即时间分辨率往往无法同时解决时间差。