Introduction
- Causality: Objectives and Assessment
- Isabelle Guyon, Dominik Janzing, and Bernhard Schölkopf; 6:1-42, 2010.
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Fundamentals and Algorithms
- Causal Inference
- Judea Pearl; 6:39-58, 2010.
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- Beware of the DAG!
- A. Philip Dawid; 6:59-86, 2010.
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- Causal Discovery as a Game
- Frederick Eberhardt; 6:87-96, 2010.
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- Sparse Causal Discovery in Multivariate Time Series
- Stefan Haufe, Klaus-Robert Müller, Guido Nolte, Nicole Krämer; 6:97-106, 2010.
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- Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
- Jan Lemeire, Kris Steenhaut; 6:107-120, 2010.
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- Bayesian Algorithms for Causal Data Mining
- Subramani Mani, Constantin F. Aliferis, Alexander Statnikov; 6:121-136, 2010.
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- When causality matters for prediction
- Robert E. Tillman, Peter Spirtes; 6:137-146, 2010.
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Challenge contributions
Cause Effect Pairs task (Pairs of variables with known cause-effect relationships)
- Distinguishing between cause and effect
- Joris Mooij, Dominik Janzing; 6:147-156, 2010.
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- Nonlinear acyclic causal models
- Kun Zhang, Aaapo Hyvärinen; 6:157-164, 2010.
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CYTO task (Protein signaling networks in human T-cells)
- Recovering Cyclic Causal Structure
- Sleiman Itani, Mesrob Ohannessian, Karen Sachs, Garry P. Nolan, Munther A. Dahleh; 6:165-176, 2010.
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- Causal learning without DAGs
- David Duvenaud, Daniel Eaton, Kevin Murphy, Mark Schmidt; 6:177-190, 2010.
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LOCANET tasks (Four tasks in genomics, socio-economics, and chemo-informatics)
- Discover Local Causal Network around a Target to a Given Depth
- You Zhou, Changzhang Wang, Jianxin Yin, Zhi Geng; 6:191-202, 2010.
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- Fast Committee-Based Structure Learning
- Ernest Mwebaze, John A. Quinn; 6:203-214, 2010.
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SIGNET task (Plant signaling network)
- SIGNET: Boolean Rile Deetermination for Abscisic Acid Signaling
- Jerry Jenkins; 6:215-224, 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; 6:225-236, 2010.
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- Reverse Engineering of Asynchronous Boolean Networks
- Cheng Zheng, Zhi Geng; 6:237-248, 2010.
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TIED task (Artificial)
- TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries
- Alexander Statnikov, Constantin F. Aliferis; 6:249-256, 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; 6:257-266, 2010.
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NOISE task (Neurophysiology)
- Comparison of Granger Causality and Phase Slope Index
- Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, Klaus-Robert Müller; 6:267-276, 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; 6:277-288, 2010.
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