causal discovery

1. Data Analytics Group (DAG)
Research Topics

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