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.


Mini Symposium
Thurday, December 10, 2009
Hyatt Regency, Vancouver, Canada

Session 1.

1.30 pm - Welcome and introduction

1.35 pm - Halbert White and Xun Lu. Granger causality and dynamic structural systems [Abstract][Slides].

2:15 pm - Florin Popescu and Guido Nolte. Time series causality inference using the Phase Slope Index [Abstract][Slides].


2:40 pm - Coffee break


Session 2.

3:00 pm - Alard Roebroeck and Rainer Goebel. Granger causality in brain connectivity studies using functional Magnetic Resonance Imaging (fMRI) data [Abstract][Slides].

3:25 pm - Alessio Moneta. Graphical Causal Models for Time Series Econometrics: Some Recent Developments and Applications [Abstract][Slides].

3:50 pm - Isabelle Guyon. Open-access datasets for time series causality discovery validation [Abstract][Slides].

Links to related workshops/competitions

NIPS 2008 causality workshop: objectives and assessment. The second challenge in causality organized by the causality workbench.

WCCI  2008 causation and prediction challenge. A first activity of the causality workbench.

NIPS 2006 workshop on causality and feature selection. The ancestor of this workshop.

IJCNN 2007 Agnostic learning vs. Prior knowledge challenge. “When everything fails, ask for additional domain knowledge” is the current motto of machine learning. Therefore, assessing the real added value of prior/domain knowledge is a both deep and practical question.The participants competed in two track: the “prior knowledge track” for which they had access to the raw data and information about the data, and the “agnostic learning track” for which they had access to preprocessed data with no knowledge of the identity of the features.

WCCI 2006 performance prediction challenge. “How good are you at predicting how good you are? 145 participants tried to answer that question. Cross-validation came very strong. Can you do better? Measure yourself against the winners by participating to the model selection game.

NIPS 2003 workshop on feature extraction and feature selection challenge. We organized a competition on five data sets in which hundreds of entries were made. The web site of the challenge is still available for post challenge submissions. Measure yourself against the winners! See the book we published with a CD containing the datasets, tutorials, papers on s.o.a. methods.

Pascal challenges: The Pascal network is sponsoring several challenges in Machine learning.

Data mining competitions:
A list of data mining competitions maintained by KDnuggets, including the well known KDD cup.

List of data sets for machine learning:
A rather comprehensive list maintained by MLnet.

UCI machine learning repository: A great collection of datasets for machine learning research.

DELVE: A platform developed at University of Torontoto benchmark machine learning algorithms.

Critical Assessment of Microarray Data Analysis, an annual conference on gene expression microarray data analysis. This conference includes a context with emphasis on gene selection, a special case of feature selection.

International Conference on Document Analysis and Recognition, a bi-annual conference proposing a contest in printed text recognition. Feature extraction/selection is a key component to win such a contest.

Text Retrieval conference, organized every year by NIST. The conference is organized around the result of a competition. Past winners have had to address feature extraction/selection effectively.

In conjunction with the International Conference on Pattern Recognition, ICPR 2004, a face recognition contest is being organized.

An important competition in protein structure prediction called Critical Assessment of
 Techniques for Protein Structure Prediction.

Contact information

Workshop organisors: Florin C. Popescu and Guido Nolte (Fraunhofer FIRST, Germany) and Isabelle Guyon (Clopinet, USA).

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).


causality @ clopinet . com


Causality Microsoft Fraunhofer Clopinet