2016/05/31 by Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf +2 · 1 citation
Chemistry · Computer Science · Mathematics · Neuroscience · Psychology · #Algorithm #Artificial intelligence #Causal inference #Causal model #Computer science #Econometrics #Electrochemical Analysis and Applications #Electroencephalography #Functional Brain Connectivity Studies #Independent component analysis #Inference #Linear model #Machine learning #Mathematics #Neural dynamics and brain function #Pattern recognition (psychology) #Psychology #Statistics #Variable (mathematics) #cs.LG #stat.AP #stat.ME #stat.ML
paper · pdf · doi:10.1109/prni.2016.7552331
published as Pattern Recognition in Neuroimaging (PRNI), International Workshop on, 1-4, 2016 · arXiv admin note: text overlap with arXiv:1512.01255
openalex publication_date 2016/06/01 · arxiv created 2016/09/30 · arxiv updated 2016/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.