JMLR Workshop and Conference Proceedings: Volume 6

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


Editors: Isabelle Guyon, Dominik Janzing, and Bernhard Schölkopf.

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Supplemental material (challenge, datasets, fact sheets)

Introduction

Causality: Objectives and Assessment
Isabelle Guyon, Dominik Janzing, and Bernhard Schölkopf; 6:1-42, 2010.
[abs] [pdf]

Fundamentals and Algorithms

Causal Inference
Judea Pearl; 6:39-58, 2010.
[abs] [pdf]

Beware of the DAG!
A. Philip Dawid; 6:59-86, 2010.
[abs] [pdf]

Causal Discovery as a Game
Frederick Eberhardt; 6:87-96, 2010.
[abs] [pdf]

Sparse Causal Discovery in Multivariate Time Series
Stefan Haufe, Klaus-Robert Müller, Guido Nolte, Nicole Krämer; 6:97-106, 2010.
[abs] [pdf]
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
Jan Lemeire, Kris Steenhaut; 6:107-120, 2010.
[abs] [pdf]
Bayesian Algorithms for Causal Data Mining
Subramani Mani, Constantin F. Aliferis, Alexander Statnikov; 6:121-136, 2010.
[abs] [pdf]
When causality matters for prediction
Robert E. Tillman, Peter Spirtes; 6:137-146, 2010.
[abs] [pdf]

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.
[abs] [pdf]
Nonlinear acyclic causal models
Kun Zhang, Aaapo Hyvärinen; 6:157-164, 2010.
[abs] [pdf]

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.
[abs] [pdf]
Causal learning without DAGs
David Duvenaud, Daniel Eaton, Kevin Murphy, Mark Schmidt; 6:177-190, 2010.
[abs] [pdf]

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.
[abs] [pdf]
Fast Committee-Based Structure Learning
Ernest Mwebaze, John A. Quinn; 6:203-214, 2010.
[abs] [pdf]

SIGNET task (Plant signaling network)


SIGNET: Boolean Rile Deetermination for Abscisic Acid Signaling
Jerry Jenkins; 6:215-224, 2010.
[abs] [pdf]
The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data
Mehreen Saeed; 6:225-236, 2010.
[abs] [pdf]
Reverse Engineering of Asynchronous Boolean Networks
Cheng Zheng, Zhi Geng; 6:237-248, 2010.
[abs] [pdf]

TIED task (Artificial)


TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries
Alexander Statnikov, Constantin F. Aliferis; 6:249-256, 2010.
[abs] [pdf]

MIDS task (Artificial dymanic system)


Learning Causal Models That Make Correct Manipulation Predictions
Mark Voortman, Denver Dash, Marek J. Druzdzel; 6:257-266, 2010.
[abs] [pdf]

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.
[abs] [pdf]

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.
[abs] [pdf]

Acknowlegements: We are grateful to Karin Bierig for her help with formatting these proceedings.



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