NIPS Workshop on Causality: Objectives and Assessment, Whistler 2008

NIPS Workshop on Causality: Objectives and Assessment, Whistler 2008

11 Videos · Dec 12, 2008

About

Machine learning has traditionally been focused on prediction. Given observations that have been generated by an unknown stochastic dependency, the goal is to infer a law that will be able to correctly predict future observations generated by the same dependency. Statistics, in contrast, has traditionally focused on data modeling, i.e., on the estimation of a probability law that has generated the data. During recent years, the boundaries between the two disciplines have become blurred and both communities have adopted methods from the other, however, it is probably fair to say that neither of them has yet fully embraced the field of causal modeling, i.e., the detection of causal structure underlying the data. Since the Eighties there has been a community of researchers, mostly from statistics and philosophy, who have developed methods aiming at inferring causal relationships from observational data. While this community has remained relatively small, it has recently been complemented by a number of researchers from machine learning.

The goal of this workshop is to discuss new approaches to causal discovery from empirical data, their applications and methods to evaluate their success. Emphasis will be put on the definition of objectives to be reached and assessment methods to evaluate proposed solutions. The participants are encouraged to participate in a ""competition pot-luck"" in which datasets and problems will be exchanged and solutions proposed.

More information about the workshop can be found here.

Videos

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24:18

Causal models as conditional density models

Kevin P. Murphy

Dec 20, 2008

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4835 views

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22:22

Beware of the DAG!

Phil Dawid

Dec 20, 2008

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5517 views

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16:50

When causality matters for prediction:Investigating the practical tradeoffs

Peter Spirtes,

Robert E. Tillman

Dec 22, 2008

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3978 views

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01:27:34

Causal Inference as Computational Learning

Judea Pearl

Dec 20, 2008

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9072 views

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14:38

Welcome and program presentation, short overview over the posters

Dominik Janzing

Dec 20, 2008

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3509 views

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18:37

Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models

Aapo Hyvärinen,

Kun Zhang

Dec 22, 2008

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4879 views

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14:48

Causal Directions in Noisy Environment

Guido Nolte

Dec 22, 2008

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3646 views

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17:10

Benchmarks, wikis, and open-source causal discovery

Patrik Hoyer

Dec 20, 2008

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3533 views

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21:36

Competition Results

Isabelle Guyon

Dec 20, 2008

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4329 views

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33:59

Causal Structure Search: Philosophical Foundations and Future Problems

Richard Scheines,

Peter Spirtes

Dec 20, 2008

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4751 views

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23:36

Analysis of the binary instrumental variable model

Thomas Richardson

Dec 20, 2008

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3937 views