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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

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

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Editors: Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf

[bib][citeproc]

Contents:

  • Introduction
  • Fundamentals and Algorithms
  • Challenge Contributions

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Introduction

Causality: Objectives and Assessment

; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:1-42

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Fundamentals and Algorithms

Causal Inference

Judea Pearl; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:39-58

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Beware of the DAG!

A. Philip Dawid; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:59-86

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Causal Discovery as a Game

Frederick Eberhardt; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:87-96

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Sparse Causal Discovery in Multivariate Time Series

Stefan Haufe, Klaus-Robert Müller, Guido Nolte, Nicole Krämer; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:97-106

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

Jan Lemeire, Kris Steenhaut; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:107-120

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Bayesian Algorithms for Causal Data Mining

Subramani Mani, Constantin F. Aliferis, Alexander Statnikov; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:121-136

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When causality matters for prediction: investigating the practical tradeoffs

Robert E. Tillman, Peter Spirtes; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:137-146

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Challenge Contributions

Distinguishing between cause and effect

Joris Mooij, Dominik Janzing; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:147-156

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Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models

Kun Zhang, Aapo Hyvärinen; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:157-164

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Structure Learning in Causal Cyclic Networks

Sleiman Itani, Mesrob Ohannessian, Karen Sachs, Garry P. Nolan, Munther A. Dahleh; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:165-176

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Causal learning without DAGs

David Duvenaud, Daniel Eaton, Kevin Murphy, Mark Schmidt; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:177-190

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Discover Local Causal Network around a Target to a Given Depth

You Zhou, Changzhang Wang, Jianxin Yin, Zhi Geng; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:191-202

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Fast Committee-Based Structure Learning

Ernest Mwebaze, John A. Quinn; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:203-214

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SIGNET: Boolean Rule Determination for Abscisic Acid Signaling

Jerry Jenkins; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:215-224

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

Mehreen Saeed; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:225-236

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

Cheng Zheng, Zhi Geng; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:237-248

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TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries

Alexander Statnikov, Constantin F. Aliferis; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:249-256

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

Mark Voortman, Denver Dash, Marek J. Druzdzel; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:257-266

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Comparison of Granger Causality and Phase Slope Index

Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, Klaus-Robert Müller; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:267-276

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

Michael McCann, Yuhua Li, Liam Maguire, Adrian Johnston; Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:277-288

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