功能连接研究中误导性因果陈述的诱惑
The lure of misleading causal statements in functional connectivity research
摘要：
作为神经科学家，我们希望了解大脑内的因果相互作用或机制如何引起感知、认知和行为。通常使用统计技术（如功能连接、格兰杰因果关系或信息流）从测量的活动中估计相互作用效应，其结果往往被错误地视为揭示了机械的洞察力。由于这些统计技术将模型拟合到大脑的低维测量值，因此它们忽略了大脑活动是高维的这一事实。在这里，我们关注的是常见输入的明显混淆：无数未观察到的变量可能比少数观察到的变量更有影响力。任何给定的观察到的相关性都可以通过考虑未观察到的变量的无限因果模型来解释。因此，大量欠采样测量中的相关性告诉我们关于机制的信息很少。我们认为，这些因果关系的相关性的错误推断是由一个隐含的重新定义的话，建议机制，如连接，因果关系，和流增强。
英文摘要：
As neuroscientists we want to understand how causal interactions or mechanisms within the brain give rise to perception, cognition, and behavior. It is typical to estimate interaction effects from measured activity using statistical techniques such as functional connectivity, Granger Causality, or information flow, whose outcomes are often falsely treated as revealing mechanistic insight. Since these statistical techniques fit models to low-dimensional measurements from brains, they ignore the fact that brain activity is high-dimensional. Here we focus on the obvious confound of common inputs: the countless unobserved variables likely have more influence than the few observed ones. Any given observed correlation can be explained by an infinite set of causal models that take into account the unobserved variables. Therefore, correlations within massively undersampled measurements tell us little about mechanisms. We argue that these mis-inferences of causality from correlation are augmented by an implicit redefinition of words that suggest mechanisms, such as connectivity, causality, and flow.
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