The 2016 ACM SIGKDD Workshop on Causal Discovery (CD 2016)
When : 2016-08-14
Where : California
Submission Deadline : 2016-05-16
Categories : Data Mining , Machine Learning
The 2016 ACM SIGKDD Workshop on Causal Discovery (CD 2016)
August 14, 2016
California
Call For Papers
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 an 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.
Inspired by such achievements, this workshop aims to provide a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. Based on the platform of KDD, this workshop is especially interested in attracting contributions that link data mining/machine learning research with causal discovery, and solutions to causal discovery in large scale data sets.
Topics
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- 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