From 27.09.2009 to 02.10.2009, the Dagstuhl Seminar 09401 ``Machine learning approaches to statistical dependences and causality\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available
“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answ...
Discovering statistical representations and relations among random variables is a very important tas...
The relationship between statistical dependency and causality lies at the heart of all statistical a...
From 27.09.2009 to 02.10.2009, the Dagstuhl Seminar 09401 ``Machine learning approaches to statistic...
Machine learning has traditionally been focused on prediction. Given observations that have been gen...
Causality is a complex concept, which roots its developments across several fields, such as statisti...
Causal inference -- the process of drawing a conclusion about the impact of an exposure on an outcom...
This dissertation consists of three papers sharing the objective to analyze how machine learning met...
In this talk I will, first, introduce the so-called causal discovery task, that is, the task of lear...
The so-called kernel-based tests of independence are developed for automatic causal discovery betwee...
From April 14 -- 20, 2007, the Dagstuhl Seminar 07161 ``Probabilistic, Logical and Relational Learni...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
University of Technology Sydney. Faculty of Engineering and Information Technology.A main goal of st...
The two fields of machine learning and graphical causality arose and are developed separately. Howev...
Inferring a system’s underlying mechanisms is a primary goal in many areas of science. For instance,...
“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answ...
Discovering statistical representations and relations among random variables is a very important tas...
The relationship between statistical dependency and causality lies at the heart of all statistical a...
From 27.09.2009 to 02.10.2009, the Dagstuhl Seminar 09401 ``Machine learning approaches to statistic...
Machine learning has traditionally been focused on prediction. Given observations that have been gen...
Causality is a complex concept, which roots its developments across several fields, such as statisti...
Causal inference -- the process of drawing a conclusion about the impact of an exposure on an outcom...
This dissertation consists of three papers sharing the objective to analyze how machine learning met...
In this talk I will, first, introduce the so-called causal discovery task, that is, the task of lear...
The so-called kernel-based tests of independence are developed for automatic causal discovery betwee...
From April 14 -- 20, 2007, the Dagstuhl Seminar 07161 ``Probabilistic, Logical and Relational Learni...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
University of Technology Sydney. Faculty of Engineering and Information Technology.A main goal of st...
The two fields of machine learning and graphical causality arose and are developed separately. Howev...
Inferring a system’s underlying mechanisms is a primary goal in many areas of science. For instance,...
“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answ...
Discovering statistical representations and relations among random variables is a very important tas...
The relationship between statistical dependency and causality lies at the heart of all statistical a...