This symposium addresses a topic that has spurred vigorous scientific debate of late in the fields of neuroscience and machine learning: causality in time-series data. In neuroscience, causal inference in brain signal activity (EEG, MEG, fMRI, etc.) is challenged by relatively rough prior knowledge of brain connectivity and by sensor limitations (mixing of sources). On the machine learning side, as the Causality workshop last year’s NIPS conference has evidenced for static (non-time series) data, there are issues of whether or not graphical models (directed acyclic graphs) pioneered by Judea Pearl, Peter Spirtes, and others can reliably provide a cornerstone of causal inference, whereas in neuroscience there are issues of whether Granger type causality inference is appropriate given the source mixing problem, traditionally addressed by ICA methods. Further topics, yet to be fully explored, are non-linearity, non-Gaussianity and full causal graph inference in high-dimensional time series data. Many ideas in causality research have been developed by and are of direct interest and relevance to researchers from fields beyond ML and neuroscience: economics (i.e. the Nobel Prize winning work of the late Clive Granger, which we will pay tribute to), process and controls engineering, sociology, etc. Despite the long-standing challenges of time-series causality, both theoretical and computational, the recent emergence of cornerstone developments and efficient computational learning methods all point to the likely growth of activity in this seminal topic.
Along with the stimulating discussion of recent research on time-series causality, we will present and highlight time-series datasets added to the Causality Workbench, which have grown out of last year’s Causality challenge and NIPS workshop, some of which are neuroscience related.
- Luiz Baccala (Escola Politecnica da Universidade de Sao Paulo, Brazil)
- Katarina Blinowska (University of Warsaw, Poland)
- Alessio Moneta (Max Planck Institute of Economics, Germany)
- Mischa Rosenblum (Potsdam University, Germany)
- Bjoern Schelter (Freiburg Center for Data Analysis and Modeling, Germany)
- Pedro Valdes-Sosa (Neurosciences Center of Cuba)