论文标题
摇动因果树：关于忠诚和最小化的假设，超越了成对互动
Shaking the causal tree: On the faithfulness and minimality assumptions beyond pairwise interactions
论文作者
论文摘要
所谓的因果发现算法以因果忠诚的概念为基础，提出了相互信息（MI）和条件互信息（CMI）的分解，以揭示因果影响的变量集。这些算法在连接链接时缺乏会计新出现原因，从而导致对复杂系统组织的虚假修饰。在这里，我们表明CMI中必须包含因果新兴信息。我们还将这一结果与对忠诚的内在侵犯联系起来，并阐明了因果关系最小的概念的重要性。最后，我们展示了忠诚是如何被错误地假定的，这仅是因为在原则上提供了一个非双向系统的示例，从原则上讲应该违反忠实，但事实并非如此。净结果提出了有关因果发现算法的更新，该算法原则上可以检测并隔离网络重建问题中未发现的网络重建问题的新兴因果影响。
Built upon the concept of causal faithfulness, the so-called causal discovery algorithms propose the breakdown of mutual information (MI) and conditional mutual information (CMI) into sets of variables to reveal causal influences. These algorithms suffer from the lack of accounting emergent causes when connecting links, resulting in a spuriously embellished view of the organization of complex systems. Here, we show that causal emergent information is necessarily contained in CMIs. We also connect this result with the intrinsic violation of faithfulness and elucidate the importance of the concept of causal minimality. Finally, we show how faithfulness can be wrongly assumed only because of the appearance of spurious correlations by providing an example of a non-pairwise systems which should violate faithfulness, in principle, but it does not. The net result proposes an update to causal discovery algorithms, which can, in principle, detect and isolate emergent causal influences in the network reconstruction problems undetected so far.
