Design and Analysis of the Causation and Prediction Challenge

Isabelle Guyon, Constantin Aliferis, Greg Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, Alexander Statnikov
Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, PMLR 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).

Cite this Paper

BibTeX
@InProceedings{pmlr-v3-guyon08a, title = {Design and Analysis of the Causation and Prediction Challenge}, author = {Guyon, Isabelle and Aliferis, Constantin and Cooper, Greg and Elisseeff, André and Pellet, Jean-Philippe and Spirtes, Peter and Statnikov, Alexander}, booktitle = {Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008}, pages = {1--33}, year = {2008}, editor = {Guyon, Isabelle and Aliferis, Constantin and Cooper, Greg and Elisseeff, André and Pellet, Jean-Philippe and Spirtes, Peter and Statnikov, Alexander}, volume = {3}, series = {Proceedings of Machine Learning Research}, address = {Hong Kong}, month = {03--04 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v3/guyon08a/guyon08a.pdf}, url = {http://proceedings.mlr.press/v3/guyon08a.html}, 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).} }
Endnote
%0 Conference Paper %T Design and Analysis of the Causation and Prediction Challenge %A Isabelle Guyon %A Constantin Aliferis %A Greg Cooper %A André Elisseeff %A Jean-Philippe Pellet %A Peter Spirtes %A Alexander Statnikov %B Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 %C Proceedings of Machine Learning Research %D 2008 %E Isabelle Guyon %E Constantin Aliferis %E Greg Cooper %E André Elisseeff %E Jean-Philippe Pellet %E Peter Spirtes %E Alexander Statnikov %F pmlr-v3-guyon08a %I PMLR %P 1--33 %U http://proceedings.mlr.press/v3/guyon08a.html %V 3 %X 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).
RIS
TY - CPAPER TI - Design and Analysis of the Causation and Prediction Challenge AU - Isabelle Guyon AU - Constantin Aliferis AU - Greg Cooper AU - André Elisseeff AU - Jean-Philippe Pellet AU - Peter Spirtes AU - Alexander Statnikov BT - Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 DA - 2008/12/31 ED - Isabelle Guyon ED - Constantin Aliferis ED - Greg Cooper ED - André Elisseeff ED - Jean-Philippe Pellet ED - Peter Spirtes ED - Alexander Statnikov ID - pmlr-v3-guyon08a PB - PMLR DP - Proceedings of Machine Learning Research VL - 3 SP - 1 EP - 33 L1 - http://proceedings.mlr.press/v3/guyon08a/guyon08a.pdf UR - http://proceedings.mlr.press/v3/guyon08a.html AB - 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). ER -
APA
Guyon, I., Aliferis, C., Cooper, G., Elisseeff, A., Pellet, J., Spirtes, P. & Statnikov, A.. (2008). Design and Analysis of the Causation and Prediction Challenge. Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, in Proceedings of Machine Learning Research 3:1-33 Available from http://proceedings.mlr.press/v3/guyon08a.html.

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