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

Causal models as conditional density models
Dec 20, 2008
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4835 views

Beware of the DAG!
Dec 20, 2008
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5517 views

When causality matters for prediction:Investigating the practical tradeoffs
Dec 22, 2008
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3978 views

Causal Inference as Computational Learning
Dec 20, 2008
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9072 views

Welcome and program presentation, short overview over the posters
Dec 20, 2008
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3509 views

Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models
Dec 22, 2008
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4879 views

Causal Directions in Noisy Environment
Dec 22, 2008
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3646 views

Benchmarks, wikis, and open-source causal discovery
Dec 20, 2008
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3533 views

Competition Results
Dec 20, 2008
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4329 views

Causal Structure Search: Philosophical Foundations and Future Problems
Dec 20, 2008
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4751 views

Analysis of the binary instrumental variable model
Dec 20, 2008
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3937 views