标题： MERLiN: 线性网络中的混合效应恢复 显示英文标题

标题： MERLiN: Mixture Effect Recovery in Linear Networks

摘要： 因果推理关注变量之间的因果关系识别，例如确定某一刺激是否会影响某个大脑区域的活动。然而，观察到的变量本身通常并不构成有意义的因果变量，因此需要考虑线性组合。 例如，在脑电图研究中，人们感兴趣的是建立皮质信号（因果变量）之间的因果关系，这些信号可以通过电极信号的线性组合恢复，而不是建立电极信号（观察变量）之间的因果关系。 我们引入了MERLiN（Linear Networks中的混合效应恢复），这是一组因果推理算法家族，它们实现了一种从非因果变量构造因果变量的新方法。 我们通过将其应用于EEG数据展示了基本的MERLiN算法如何可以扩展以适用于不同的（神经影像学）数据模态。给定一个观测到的线性混合变量，这些算法能够恢复一个作为另一给定变量线性效应的因果变量。 也就是说，MERLiN允许我们恢复一个受到特定大脑区域活动影响的皮质信号，而该信号并非刺激的直接效应。 所有展示算法的Python/Matlab实现可以在https://github.com/sweichwald/MERLiN获取。 显示英文摘要

摘要： Causal inference concerns the identification of cause-effect relationships between variables, e.g. establishing whether a stimulus affects activity in a certain brain region. The observed variables themselves often do not constitute meaningful causal variables, however, and linear combinations need to be considered. In electroencephalographic studies, for example, one is not interested in establishing cause-effect relationships between electrode signals (the observed variables), but rather between cortical signals (the causal variables) which can be recovered as linear combinations of electrode signals. We introduce MERLiN (Mixture Effect Recovery in Linear Networks), a family of causal inference algorithms that implement a novel means of constructing causal variables from non-causal variables. We demonstrate through application to EEG data how the basic MERLiN algorithm can be extended for application to different (neuroimaging) data modalities. Given an observed linear mixture, the algorithms can recover a causal variable that is a linear effect of another given variable. That is, MERLiN allows us to recover a cortical signal that is affected by activity in a certain brain region, while not being a direct effect of the stimulus. The Python/Matlab implementation for all presented algorithms is available on https://github.com/sweichwald/MERLiN