PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Design and Analysis of the Causation and Prediction Challenge
Isabelle Guyon, Constantin Aliferis, Greg Cooper, Andre Elisseeff, Jean-Philippe Pellet, Peter Spirtes and Alexander Statnikov
In: WCCI 2008 causality workshop, 3-4 June 2008, Hong-Kong, China.

Abstract

We organized for WCCI 2008 a challenge to evaluate causal modeling techniques, focusing on predicting the effect of "interventions" performed by an external agent. Examples of that problem are found in the medical domain to predict the effect of a drug prior to administering it, or in econometrics to predict the effect of a new policy prior to issuing it. We concentrate on a given target variable to be predicted (e.g., health status of a patient) from a number of candidate predictive variables or "features" (e.g., risk factors in the medical domain). Under interventions, variable predictive power and causality are tied together. For instance, both smoking and coughing may be predictive of lung cancer (the target) in the absence of external intervention; however, prohibiting smoking (a possible cause) may prevent lung cancer, but administering a cough medicine to stop coughing (a possible consequence) would not. We propose four tasks from various application domains, each dataset including a training set drawn from a \natural" distribution and three test sets: one from the same distribution as the training set and two corresponding to data drawn when an external agent is manipulating certain variables. The goal is to predict a binary target variable, whose values on test data are withheld. The participants were asked to provide predictions of the target variable on test data and the list of variables (features) used to make predictions. The challenge platform remains open for post-challenge submissions and the organization of other events is under way (see http://clopinet.com/causality).

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EPrint Type:Conference or Workshop Item (Oral)
Additional Information:This paper is complemented by a technical report entitled: Datasets of the Causation and Prediction Challenge
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4565
Deposited By:Isabelle Guyon
Deposited On:13 March 2009