UAI Causality Workshop 2014 : UAI 2014 Workshop Causal Learning: Inference and Prediction
Call For Papers
2nd Call for Papers
Workshop "Causal Inference: Learning and Prediction"
Uncertainty in Artificial Intelligence 2014 (UAI 2014)
Sunday, July 27, 2014
Quebec City, Quebec, Canada
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, both for causal structure learning and
for making causal predictions (i.e., predicting what happens under
interventions). 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 inference from observational and interventional data,
especially when latent (confounding) variables or feedback loops may be
present. Contributions describing practical applications of causal methods are
specially encouraged. This one-day workshop will explore these topics through a
set of invited talks, presentations and a poster session.
We encourage co-submission of (full) papers that have been submitted to the
main UAI 2014 conference.
See our website for updates:
[This workshop takes place directly after the 30th Conference on Uncertainty in
Artificial Intelligence (UAI), 23-26 July, 2014.]
* Addressing the challenge of practical causal inference in the context of real applications;
* Developing measures and methods for evaluating the quality of causal predictions;
* Feasible prediction of post-interventional distributions by reconstructing latent confounders;
* Considering the relative robustness of assumptions and algorithms to model misspecification;
* Methods for causal inference from high-dimensional data;
* Methods for combining different datasets;
* Experimental design for causal inference;
* Real-world validation of causal inference methods;
* Discussions on the possibility of making causal predictions in a highly confounded and cyclic world;
* Occam’s Razor in causal inference (methodological justifications for oversimplified models).
There are two possible submission formats. The authors can either submit:
- a one-page abstract (including references) describing recently published work, or
- a full-length paper, limited to 9 pages (including figures and text, excluding references).
If a contribution consists of material that has been published elsewhere earlier on
(except possibly at UAI 2014), the authors must choose the one-page abstract format
and cite the original work.
Our submission deadline comes a few days after the UAI author notification
deadline. We encourage co-submission of (full) papers that have been submitted
to the main UAI 2014 conference. Please indicate if your paper was also
submitted to UAI. If accepted for UAI, the paper would be published in UAI
proceedings, but we may also invite the authors to give a (oral or poster)
presentation at the workshop.
Style files for full papers can be found on the UAI website: http://auai.org/uai2014/
Abstracts and papers must be submitted via e-mail before the deadline (June 6) to:
Contributions will be peer reviewed by at least two reviewers. Accepted
papers will be presented either as oral presentation or in a poster session.
After the workshop we will publish proceedings on CEUR-WS and via the web-page:
Authors of accepted papers can choose to contribute the submitted manuscript
(i.e., the full paper or the abstract). They can also choose not to contribute
to the proceedings.
Oral presentation slides will also be disseminated via the workshop web-page.
* June 6 2014: Submission deadline for abstracts and full papers
* June 27 2014: Author notification
* July 27 2014: Workshop (following the UAI 2014 main conference, July 24-26)
Joris Mooij (Chair), University of Amsterdam
Dominik Janzing, Max Planck Institute of Intelligent Systems
Jonas Peters, ETH Zürich
Tom Claassen, Radboud University Nijmegen
Antti Hyttinen, California Institute of Technology