Volume 6, February 2010

Causality: Objectives and Assessment (NIPS 2008 Workshop)
December 12, 2008, Whistler, Canada

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  Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf: Causality: Objectives and Assessment. 1-42

Fundamentals and Algorithms

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  Judea Pearl: Causal Inference. 39-58
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  A. Philip Dawid: Beware of the DAG! 59-86
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  Frederick Eberhardt: Causal Discovery as a Game. 87-96
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  Stefan Haufe, Klaus-Robert Müller, Guido Nolte, Nicole Krämer: Sparse Causal Discovery in Multivariate Time Series. 97-106
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  Jan Lemeire, Kris Steenhaut: Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions. 107-120
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  Subramani Mani, Constantin F. Aliferis, Alexander R. Statnikov: Bayesian Algorithms for Causal Data Mining. 121-136
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  Robert E. Tillman, Peter Spirtes: When causality matters for prediction. 137-146

Challenge contributions
Cause Effect Pairs task (Pairs of variables with known cause-effect relationships)

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  Joris M. Mooij, Dominik Janzing: Distinguishing between cause and effect. 147-156
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  Kun Zhang, Aapo Hyvärinen: Nonlinear acyclic causal models. 157-164

CYTO task (Protein signaling networks in human T-cells)

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  Sleiman Itani, Mesrob I. Ohannessian, Karen Sachs, Garry P. Nolan, Munther A. Dahleh: Recovering Cyclic Causal Structure. 165-176
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  David K. Duvenaud, Daniel Eaton, Kevin P. Murphy, Mark W. Schmidt: Causal learning without DAGs. 177-190

LOCANET tasks (Four tasks in genomics, socio-economics, and chemo-informatics)

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  You Zhou, Changzhang Wang, Jianxin Yin, Zhi Geng: Discover Local Causal Network around a Target to a Given Depth. 191-202
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  Ernest Mwebaze, John A. Quinn: Fast Committee-Based Structure Learning. 203-214

SIGNET task (Plant signaling network)

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  Jerry Jenkins: SIGNET: Boolean Rile Deetermination for Abscisic Acid Signaling. 215-224
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  Mehreen Saeed: The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data. 225-236
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  Cheng Zheng, Zhi Geng: Reverse Engineering of Asynchronous Boolean Networks. 237-248

TIED task (Artificial)

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  Alexander R. Statnikov, Constantin F. Aliferis: TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries. 249-256

MIDS task (Artificial dymanic system)

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  Mark Voortman, Denver Dash, Marek J. Druzdzel: Learning Causal Models That Make Correct Manipulation Predictions. 257-266

NOISE task (Neurophysiology)

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  Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, Klaus-Robert Müller: Comparison of Granger Causality and Phase Slope Index. 267-276

SECOM task (Manufacturing)

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  Michael McCann, Yuhua Li, Liam P. Maguire, Adrian Johnston: Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control. 277-288