Website
http://dataportal.arc.gov.au/NCGP/API/grants/DP140103617
Funding Status
Closed
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Funded Activity Summary
Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop fast and scalable data mining methods for identifying causal relationships from large and/or high dimensional data sets. The developed methods will mainly be evaluated in real world biological applications. The research outcomes will be useful in many areas for causal reasoning and decision making.Funded Activity Details
Start Date: 06-2014
End Date: 12-2017
Funding Scheme: Discovery Projects
Funding Amount: $270,000.00
Funder: Australian Research Council
Research Topics
ANZSRC Field of Research (FoR)
Pattern Recognition and Data Mining | Artificial Intelligence and Image Processing
ANZSRC Socio-Economic Objective (SEO)
Information and Communication Services not elsewhere classified |
