CaMaL 2026 - International Workshop on Causal Modeling & Machine Learning

*Causal Modeling & Machine Learning*
Workshop -AT- ICML 2014
Beijing, China, June 25/26, 2014
Web: http://people.tuebingen.mpg.de/causal-learning/
Email: causal-learning-AT-tuebingen.mpg.de
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*Important Dates*
- Submission deadline: Friday April 21, 2014
- Author notification: Friday May 9, 2014
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In the last decades, interesting advances were made in machine learning for tackling some long-standing problems in causality, such as how to distinguish cause from effect and how to infer the effect of interventions using observational data. Modern machine learning methodologies provided efficient methods for causal structure learning and powerful tools for conditional independence test, which is a key component in traditional constraint-based causal discovery.
On the other hand, causal models provide compact descriptions of the properties of data distributions, and it has recently been demonstrated that causal information can facilitate various machine learning tasks, including semi-supervised learning and domain adaptation (or transfer learning). For instance, causal analysis inspired efficient methods to characterize the information transfer across regimes, environments, and sampling schemes, which is frequently encountered in data analysis.
This workshop aims to foster the research at the intersection of causal modeling and machine learning, and will take place in Beijing, China, on June 25 or 26, 2014. In particular, we are interested in how machine learning could help develop better methods for causal discovery and inference, and moreover, how causal knowledge could help understand learning problems and inspire more natural ways to solve them.
*Topics of Interest*
The workshop is concerned with all topics at the intersection of causality and machine learning, including but not limited to
- causal structure learning and inference
- characterization of causal information in observational data
- understanding machine learning tasks in light of causality
- machine learning methods exploiting causal knowledge
- efficient causal discovery in large-scale data
- big data and causality
- real-world problems for causal analysis.
*Submission Instructions*
We encourage both full paper submissions (up to 9 pages including references) and short submissions (extended abstracts, up to 4 pages in length). Submissions should be in the ICML 2014 format and need not be anonymous. All accepted contributions will be available online at the workshop website, and will be selected for an oral or poster presentation.
We will also include presentations on recently published high-impact work; to give such presentations, please submit a one-page abstract together with published papers for review. please submit your contribution at via EasyChair (https://www.easychair.org/conferences/?conf=camal1...) by April 11, 2014.
Organizing Committee
- Kun Zhang (MPI for Intelligent Systems, Germany)
- Bernhard Schölkopf (MPI for Intelligent Systems, Germany)
- Elias Bareinboim (University of California, Los Angeles, USA)
- Jiji Zhang (Lingnan University, Hong Kong)
For further information, please visit the website http://people.tuebingen.mpg.de/causal-learning/ or contact the organizing committee by email: causal-learning-AT-tuebingen.mpg.de .