Causation and Prediction Challenge

Isabelle Guyon

Abstract


We have organized a challenge on causality and will present its goals and its design to stimulate participation and involve volunteers in the organization of other upcoming events.

This challenge bridges the gap between data mining/machine learning and causal discovery. Several datasets drawn from real data, or emulating real data, are provided, with the goal of making predictions under "manipulations". The setting is very similar to a usual machine learning setting: We have a training set and a test set; a target variable, whose values are concealed in test data, must be predicted. But, the test data are not distributed like the training data: some variables in test data are "manipulated" by an external agent, i.e. set to given values instead of being drawn from the "natural" distribution.

Such problems are encountered in many application domains: In medicine to predict the effect of a new treatment, in economy or ecology to predict the consequences of new issued policies, in marketing to predict customer response to marketing campaigns. Feature selection researchers should be particularly interested in that challenge. The problems posed by the challenge require finding subsets of predictive variables, taking into account whether such variables remain predictive when manipulations are performed. We anticipate that this should require the knowledge of causal relationships between variables since acting on causes of the target may result in a response change while acting on consequences should not.

Deadline April 30, 2008 http://www.causality.inf.ethz.ch/challenge.php


About the speaker: Isabelle Guyon is a researcher in machine learning and an independent consultant. Prior to starting her consulting practice in 1996, she worked at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces and invented Support Vector Machines (in collaboration with B. Boser and V. Vapnik). Isabelle Guyon holds a Ph.D. degree in Physical Sciences of the University Pierre and Marie Curie of Paris, France. She is vice-president of the Unipen foundation, action editor of the Journal of Machine Learning Research, and competition chair of the IJCNN conference.