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Causality: Objectives and Assessment - NIPS 2008 Workshop
- Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf:
Causality: Objectives and Assessment (NIPS 2008 Workshop), Whistler, Canada, December 12, 2008. JMLR Proceedings 6, JMLR.org 2010
Preface
- Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf:
Causality: Objectives and Assessment. 1-42
Fundamentals and Algorithms
- Judea Pearl:
Causal Inference. 39-58 - A. Philip Dawid:
Beware of the DAG! 59-86 - Frederick Eberhardt:
Causal Discovery as a Game. 87-96 - Stefan Haufe, Klaus-Robert Müller, Guido Nolte, Nicole Krämer:
Sparse Causal Discovery in Multivariate Time Series. 97-106 - Jan Lemeire, Kris Steenhaut:
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions. 107-120 - Subramani Mani, Constantin F. Aliferis, Alexander R. Statnikov:
Bayesian Algorithms for Causal Data Mining. 121-136 - 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)
- Joris M. Mooij, Dominik Janzing:
Distinguishing between cause and effect. 147-156 - Kun Zhang, Aapo Hyvärinen:
Nonlinear acyclic causal models. 157-164
CYTO task (Protein signaling networks in human T-cells)
- Sleiman Itani, Mesrob I. Ohannessian, Karen Sachs, Garry P. Nolan, Munther A. Dahleh:
Recovering Cyclic Causal Structure. 165-176 - David Duvenaud, Daniel Eaton, Kevin P. Murphy, Mark Schmidt:
Causal learning without DAGs. 177-190
LOCANET tasks (Four tasks in genomics, socio-economics, and chemo-informatics)
- You Zhou, Changzhang Wang, Jianxin Yin, Zhi Geng:
Discover Local Causal Network around a Target to a Given Depth. 191-202 - Ernest Mwebaze, John A. Quinn:
Fast Committee-Based Structure Learning. 203-214
SIGNET task (Plant signaling network)
- Jerry Jenkins:
SIGNET: Boolean Rile Deetermination for Abscisic Acid Signaling. 215-224 - Mehreen Saeed:
The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data. 225-236 - Cheng Zheng, Zhi Geng:
Reverse Engineering of Asynchronous Boolean Networks. 237-248
TIED task (Artificial)
- Alexander R. Statnikov, Constantin F. Aliferis:
TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries. 249-256
MIDS task (Artificial dymanic system)
- Mark Voortman, Denver Dash, Marek J. Druzdzel:
Learning Causal Models That Make Correct Manipulation Predictions. 257-266
NOISE task (Neurophysiology)
- Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, Klaus-Robert Müller:
Comparison of Granger Causality and Phase Slope Index. 267-276
SECOM task (Manufacturing)
- Michael McCann, Yuhua Li, Liam P. Maguire, Adrian Johnston:
Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control. 277-288
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