Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
This work addresses causal inference in neuroimaging for researchers, but it is incremental as it extends a prior algorithm to handle non-linear relationships.
The paper tackles the problem of recovering non-linear cause-effect relationships from linearly mixed neuroimaging data, presenting an extended algorithm that broadens applicability and shows linear assumptions are not restrictive for EEG analysis.
Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals recorded at multiple electrodes. We recently introduced the MERLiN (Mixture Effect Recovery in Linear Networks) algorithm that is able to recover, from an observed linear mixture, a causal variable that is a linear effect of another given variable. Here we relax the assumption of this cause-effect relationship being linear and present an extended algorithm that can pick up non-linear cause-effect relationships. Thus, the main contribution is an algorithm (and ready to use code) that has broader applicability and allows for a richer model class. Furthermore, a comparative analysis indicates that the assumption of linear cause-effect relationships is not restrictive in analysing electroencephalographic data.