[Statlist] UAI causal structure learning workshop: call for papers

[Cecilia Rey]

Approaches to Causal Structure Learning Workshop
UAI 2013

Call for Papers

Monday, July 15, 2013
Bellevue, WA, USA

Causality is central to how we view and react to the world around us, to our decision making, and to the advancement of science. Causal inference in statistics and machine learning has advanced rapidly in the last 20 years, leading to a plethora of new methods. However, a side-effect of the increased sophistication of these approaches is that they have grown apart, rather than together.

The aim of this workshop is to bring together researchers interested in the challenges of causal structure learning from observational and experimental data especially when latent or confounding variables may be present.

Topics related to causal structure learning will be explored through a set of invited talks, presentations and a poster session.

[This workshop takes place directly after the 29th Conference on Uncertainty in Artificial Intelligence (UAI), 12-14 July, 2013.]

Example Topics:
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* Synthesis or comparison of alternative approaches to causal inference.
* Causal structure learning via regularization/priors.
* Using non-parametric constraints for structure learning in the presence of confounding; these may include generalized conditional independences, general polynomial constraints, or semi-algebraic approaches. 
* Methods exploiting (non-)linearity or additivity.
* Approaches for learning structure from deterministic data.
* Algorithms for learning from overlapping datasets.
* Methods for combining experimental and observational data.
* Procedures for selecting experiments.
* Methods for predicting the effects of interventions in an observed system.
* Efficient data structures and algorithms for causal structure searches.
* Methods for analyzing causal pathways.
* Applications of causal structure learning to real-world datasets.
* Experimental design for causal inference.

Paper Submission
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Submissions should be in UAI format, limited to 9 pages, excluding references.  Details are on the UAI website: http://auai.org/uai2013/ 
All papers will be peer reviewed by at least two independent referees. Abstracts and papers must be submitted via e-mail before the deadline to:

causalworkshop using gmail.com

We encourage co-submission of papers that have been submitted to the main UAI 2013 conference, please indicate if your paper was also submitted to UAI; our submission deadline comes after the UAI acceptance deadline. If accepted for UAI, the paper would be published in UAI proceedings, but we will also invite the authors to give a poster presentation at the Workshop.

Workshop format
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Broadly, this one-day workshop aims at exploring the current challenges in structure learning. It will explore these topics in presentations and invited talks. In addition, we will have a poster session highlighting new contributions and research directions.

Dissemination of results
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We will disseminate papers and talks via the workshop web-page:

http://www.statslab.cam.ac.uk/~rje42/uai13/main.htm

Important Dates
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* 30 April 2013: Abstract submission
* 11 May 2013: Full paper submission
* 31 May 2013: Author notification
* 15 July 2013: Workshop
(following the UAI 2013 main conference, Jul 12-14)

Organizers
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Robin Evans (Chair); University of Cambridge
Marloes Maathuis; ETH Zurich
Thomas Richardson; University of Washington
Ilya Shpitser; University of Southampton
Jin Tian; Iowa State University