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**Special issue on Causal Discovery **
** for the **
**Journal of Data Science and Analytics **
**(Springer, http://www.springer.com/41060)**
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Guest Editors
* Jiuyong Li (University of South Australia, jiuyong.li@unisa.edu.au)
* Kun Zhang (Carnegie Mellon University, kunz1@cmu.edu)
* Elias Bareinboim (Purdue University, eb@purdue.edu)
* Lin Liu (University of South Australia, lin.liu@unisa.edu.au)
[contact for general enquiries]

Motivation and Background
As a basic and effective tool for explanation, prediction and decision making, causal relationships have been utilized in almost all disciplines. Traditionally, causal relationships are identified by making use of interventions or randomized controlled experiments. However, conducting such experiments is often expensive or even impossible due to cost or ethical concerns. Therefore there has been increasing interest in discovering causal relationships based on observational data, and in the past few decades, significant contributions have been made to this field by computer scientists.

With the rapid accumulation of huge volumes of observational data, the field of causal discovery is seeing exciting opportunities, as well as greater challenges. On one hand, in the past 30 years, great progress has been made in the theoretical development of causal modelling and inference; on the other hand, there is a severe lack of efficient techniques for causal discovery from big data in practice. Therefore there is an urgent need to bring in data mining researchers to reap the theoretical achievements in causal discovery to transform them into efficient algorithms for various applications. At the same time, some of the key challenges for causal discovery will provide data mining researchers with exciting new research directions, such as evaluation of causal discoveries in data, and designing highly scalable algorithms for causal discovery in large data sets and heterogeneous data sets. Additionally, recent years have seen a growing interest in causal discovery in big data in many application areas, including the leading effort on this topic in biomedical research [1]. It is time for researchers in various areas to work together to solve the challenges of causal discovery and to make better use of big data for solving real life problems.

[1] G. F. Cooper et al., The Center for Causal Discovery of Biomedical Knowledge from Big Data. Journal of the American Medical Informatics Association 1-6, 2015.

Topics and Scope
* Causal discovery and structural learning
* Experimental design and causal inference from high-dimensional data
* Fusion of datasets containing heterogeneous biases (e.g., confounding, selection)
* Generalizability and extrapolation of experimental knowledge across settings
* Causal analysis in real-world problems (e.g., bioinformatics, medicine, social sciences)
* Intersection of data mining and causal inference
* Assessment of discovery methods and new datasets

Author Information
The guest editors are also organizing a causal discovery workshop (http://nugget.unisa.edu.au/CD2016/) in conjunction with the 2016 SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD 2016). They have invited a few prominent experts in causal discovery and inference as keynote speakers. Authors of papers accepted in the first round of review are invited to present their papers in the Causal Workshop with KDD 2016. Authors are free to choose not to present their papers in the workshop, and this choice does not affect the final acceptance decision of this special issue.

Authors should indicate if they wish to participate in the Causal Workshop in the covering letter of the first submission.

Review Procedure
A rigorous review will be conducted for every submission.

Authors will be given 8 weeks to improve a paper if it is provisionally accepted. A further review will be conducted after the revision to determine the acceptance/rejection of the paper.

Important Dates
* Paper submission due: 16 May 2016
* First round review notification: 13 June 2016
* Second round submission due: 7 August 2016
* Second round acceptance notification: 15 September 2016
* Final submission due: 30 September 2016
* Special issue to be published online: October/November 2016.