  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.
