On Causal Discovery With Convergent Cross Mapping
On Causal Discovery With Convergent Cross Mapping
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DOI：
10.1109/tsp.2023.3286529
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发表时间：
2023
影响因子：
5.4
通讯作者：
Kurt Butler;Guanchao Feng;P. Djurić
中科院分区：
文献类型：
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作者：
Kurt Butler;Guanchao Feng;P. Djurić
收敛交叉映射是一种原则性的信号因果发现技术，但其有效性取决于对产生信号的系统的一些假设。在这项工作中，我们对状态空间中的因果关系理论、塔肯斯定理和交叉映射进行了独立的介绍，并提出了检查信号是否适合交叉映射的条件。此外，我们提出了基于高斯过程的简单分析来测试数据中的这些条件。我们表明，我们提出的技术检测时，收敛交叉映射可能会得出错误的结果，从文献中使用的几个例子，我们评论的其他考虑，是重要的，当应用的方法，如收敛交叉映射。
Convergent cross mapping is a principled causal discovery technique for signals, but its efficacy depends on a number of assumptions about the systems that generated the signals. In this work, we present a self-contained introduction to the theory of causality in state-spaces, Takens' theorem, and cross maps, and we propose conditions to check if a signal is appropriate for cross mapping. Further, we propose simple analyses based on Gaussian processes to test for these conditions in data. We show that our proposed techniques detect when convergent cross mapping may conclude erroneous results using several examples from the literature, and we comment on other considerations that are important when applying methods such as convergent cross mapping.