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Publication Detail
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
  • Publication Type:
  • Authors:
    Weichwald S, Gretton A, Schölkopf B, Grosse-Wentrup M
  • Publisher:
  • Publication date:
  • Published proceedings:
    PRNI 2016: 6th International Workshop on Pattern Recognition in Neuroimaging
  • Name of conference:
    PRNI 2016, 6th International Workshop on Pattern Recognition in Neuroimaging
  • Conference place:
    Trento, Italy
  • Conference start date:
  • Conference finish date:
  • Keywords:
    stat.ME, stat.ME, cs.LG, stat.AP, stat.ML
  • Notes:
    arXiv admin note: text overlap with arXiv:1512.01255
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.
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