Introduction
 Causality: Objectives and Assessment
 Isabelle Guyon, Dominik Janzing, and Bernhard Schölkopf; 6:142, 2010.
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Fundamentals and Algorithms
 Causal Inference
 Judea Pearl; 6:3958, 2010.
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 Beware of the DAG!
 A. Philip Dawid; 6:5986, 2010.
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 Causal Discovery as a Game
 Frederick Eberhardt; 6:8796, 2010.
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 Sparse Causal Discovery in Multivariate Time Series
 Stefan Haufe, KlausRobert Müller, Guido Nolte, Nicole Krämer; 6:97106, 2010.
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 Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
 Jan Lemeire, Kris Steenhaut; 6:107120, 2010.
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 Bayesian Algorithms for Causal Data Mining
 Subramani Mani, Constantin F. Aliferis, Alexander Statnikov; 6:121136, 2010.
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 When causality matters for prediction
 Robert E. Tillman, Peter Spirtes; 6:137146, 2010.
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Challenge contributions
Cause Effect Pairs task (Pairs of variables with known causeeffect relationships)
 Distinguishing between cause and effect
 Joris Mooij, Dominik Janzing; 6:147156, 2010.
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 Nonlinear acyclic causal models
 Kun Zhang, Aaapo Hyvärinen; 6:157164, 2010.
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CYTO task (Protein signaling networks in human Tcells)
 Recovering Cyclic Causal Structure
 Sleiman Itani, Mesrob Ohannessian, Karen Sachs, Garry P. Nolan, Munther A. Dahleh; 6:165176, 2010.
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 Causal learning without DAGs
 David Duvenaud, Daniel Eaton, Kevin Murphy, Mark Schmidt; 6:177190, 2010.
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LOCANET tasks (Four tasks in genomics, socioeconomics, and chemoinformatics)
 Discover Local Causal Network around a Target to a Given Depth
 You Zhou, Changzhang Wang, Jianxin Yin, Zhi Geng; 6:191202, 2010.
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 Fast CommitteeBased Structure Learning
 Ernest Mwebaze, John A. Quinn; 6:203214, 2010.
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SIGNET task (Plant signaling network)
 SIGNET: Boolean Rile Deetermination for Abscisic Acid Signaling
 Jerry Jenkins; 6:215224, 2010.
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 The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data
 Mehreen Saeed; 6:225236, 2010.
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 Reverse Engineering of Asynchronous Boolean Networks
 Cheng Zheng, Zhi Geng; 6:237248, 2010.
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TIED task (Artificial)
 TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries
 Alexander Statnikov, Constantin F. Aliferis; 6:249256, 2010.
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MIDS task (Artificial dymanic system)
 Learning Causal Models That Make Correct Manipulation Predictions
 Mark Voortman, Denver Dash, Marek J. Druzdzel; 6:257266, 2010.
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NOISE task (Neurophysiology)
 Comparison of Granger Causality and Phase Slope Index
 Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, KlausRobert Müller; 6:267276, 2010.
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SECOM task (Manufacturing)
 Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control
 Michael McCann, Yuhua Li, Liam Maguire, Adrian Johnston; 6:277288, 2010.
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