Causal discovery

2. Data Analytics Group • 5 academics, 4 postdocs and 15 PhD and exchanging students. • 8 Australian Research Council Discovery projects last 10 years. • High profile publications. • Strong industry collaborations. • Currently > 1.5 millions of fund.

3. Relationship discovery for forecasting and decision making • Association analysis • Causal inference • What if reasoning • ARC project on causal relationship discovery.

4. Jixue Liu – UniSA • The mapping problem: Do the two table show the same type information. More specifically, is B the number of service years, C the salary? • The quality problem: Does the data have errors and how to detect? • The cleaning problem: What is the best way to correct the quality errors? • D2D CRC project, identity resolution and data linkage in integrating police. Data Integration, Quality, Semantics Posi SvcYrs Sal Lect 2 50 Lect 3 60 Lect 2 50 A B C Prof 2 60 Lect 5 70 Prof 3 70 Prof 2 65

5. Text mining (mining unstructured data) • NHMRC Centre of Research Excellence in Post-Marketing Surveillance of Medicines and Medical Devices. – mining of social media to identify potential signals of medication safety • D2D CRC project – Beat the news - building classification and prediction systems for civil unrest events.

6. Outlier (anomaly) detection in sensor (time series) data • SA Water – Development of smart data analytics tools to improve treatment performance assessment. • Water Research Australia – Optimisation of existing instrumentation to achieve better process performance

7. Positive correlation of birth rate to stork population • increasing the stork population would increase the birth rate?

8. A brief book http://www.springer.com/computer/ai/book/978-3-319-14432-0 Software and test data sets: http://nugget.unisa.edu.au/Causalbook/

9. Building easily interpretable causal models Big data  causal predictions Causal prediction methods

10. Causal reasoning (what if analysis) Changes of causes or context  Changes of outcomes ?

11. Causal decision trees • A decision may not be causally interpretable. 11 Provide • Classifications and • Causal explanations.

12. Causal decision tree vs decision tree 12

13. Causal discovery for feature selection

14. Causal inference– Do calculus Judea Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000. X1 X2 … Xn-1 Xn 5.2 7.5 6.5 5.2 5.6 7.2 6.6 5.3 … … … … … 5.4 7.1 7.1 5.7 5.7 6.9 6.9 5.8 +1 +0.8

15. SA Water Sensor data management StorageBinary File Database Non-standard formats Manually set Excel to load file Difficult to search and comparison Standard formats Automatic data inputs Convenient Easily searchable and comparable

16. Visualization Visualization Single plot Fragments Difficult to compare time series with different resolutions Multiple plots with different resolutions Various sources Interactive show any range zoom in and zoom out Web based no installation

17. Analysis Customized Analysis Functions Statistics and comparisons Prediction view Anomaly detection Easy for data analysis Customized functions Expandable data sources analytic tools

18. Reporting Weekly Summary Summarisation Comparison benchmarks Deviations Anomaly distributions Automatically evaluate and quantify the quality of water in a week.

19. Pattern modelling and recognition Filter anomalies Model new patterns Update new patterns

Causal discovery

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