Volume 6: Causality: Objectives and Assessment, 12 December 2008, Whistler, Canada


Editors: Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf




Causality: Objectives and Assessment

Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf; PMLR 6:1-42

Fundamentals and Algorithms

Causal Inference

Judea Pearl; PMLR 6:39-58

Beware of the DAG!

A. Philip Dawid; PMLR 6:59-86

Causal Discovery as a Game

Frederick Eberhardt; PMLR 6:87-96

Sparse Causal Discovery in Multivariate Time Series

Stefan Haufe, Klaus-Robert Müller, Guido Nolte, Nicole Krämer; PMLR 6:97-106

Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions

Jan Lemeire, Kris Steenhaut; PMLR 6:107-120

Bayesian Algorithms for Causal Data Mining

Subramani Mani, Constantin F. Aliferis, Alexander Statnikov; PMLR 6:121-136

When causality matters for prediction: investigating the practical tradeoffs

Robert E. Tillman, Peter Spirtes; PMLR 6:137-146

Challenge Contributions

Distinguishing between cause and effect

Joris Mooij, Dominik Janzing; PMLR 6:147-156

Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models

Kun Zhang, Aapo Hyvärinen; PMLR 6:157-164

Structure Learning in Causal Cyclic Networks

Sleiman Itani, Mesrob Ohannessian, Karen Sachs, Garry P. Nolan, Munther A. Dahleh; PMLR 6:165-176

Causal learning without DAGs

David Duvenaud, Daniel Eaton, Kevin Murphy, Mark Schmidt; PMLR 6:177-190

Discover Local Causal Network around a Target to a Given Depth

You Zhou, Changzhang Wang, Jianxin Yin, Zhi Geng; PMLR 6:191-202

Fast Committee-Based Structure Learning

Ernest Mwebaze, John A. Quinn; PMLR 6:203-214

SIGNET: Boolean Rule Determination for Abscisic Acid Signaling

Jerry Jenkins; PMLR 6:215-224

The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data

Mehreen Saeed; PMLR 6:225-236

Reverse Engineering of Asynchronous Boolean Networks via Minimum Explanatory Set and Maximum Likelihood

Cheng Zheng, Zhi Geng; PMLR 6:237-248

TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries

Alexander Statnikov, Constantin F. Aliferis; PMLR 6:249-256

Learning Causal Models That Make Correct Manipulation Predictions With Time Series Data

Mark Voortman, Denver Dash, Marek J. Druzdzel; PMLR 6:257-266

Comparison of Granger Causality and Phase Slope Index

Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, Klaus-Robert Müller; PMLR 6:267-276

Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control

Michael McCann, Yuhua Li, Liam Maguire, Adrian Johnston; PMLR 6:277-288

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