Design and Analysis of the Causation and Prediction Challenge
Isabelle Guyon, Constantin Aliferis, Greg Cooper, André Elisseeff,
Jean-Philippe Pellet, Peter Spirtes, and Alexander Statnikov; JMLR
W&CP 3:1-33, 2008.
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).
