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Causal Analytics for Business (BUSA90540)
Graduate courseworkPoints: 12.5On Campus (Parkville)
To learn more, visit 2023 Course and subject delivery.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Overview
Availability | May |
---|---|
Fees | Look up fees |
Data Analytics models can be used to predict a performance variable. But many business decisions are not about predicting performance per se. They are about choosing the values of key inputs, such as price or advertising spend, to optimise performance. This requires that the effects of the inputs, as coded by the model, are causal. This typically requires further assumptions about how the data was generated.
The gold standard for establishing causality is a randomised experiment, which is becoming more common in business contacts. The course covers basic principles and practice of experimentation from A-B testing to randomised incomplete block designs. All these methods give rise to estimates of causal effects.
When data is not from an experiment, associations may be entirely spurious. There are several methods that can identify causal effects, such as controlling for known confounders or identifying instrumental variables. When data is collected over time, the so-called error correction model can be used to support the case for causality.
Intended learning outcomes
On completion of this subject, students should be able to:
- Understand the key reasons that associations in non-experiment data may be spurious and to critique analyses that do not take this into account.
- Be familiar with the key principles of experimental design and how to analyse them.
- Be able to control for confounders and explain why the estimates obtained are more likely to be causal.
- Use the causal estimates to obtain an optimal decision.
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
Pre-requisite
Code | Name | Teaching period | Credit Points |
---|---|---|---|
BUSA90536 | Statistical Learning for Business |
July (On Campus - Parkville)
March (On Campus - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
Quiz - two individual in-class quizzes
| From Week 4 to Week 7 | 20% |
Syndicate assignments
| From Week 5 to Week 7 | 30% |
Final Examination
| Week 9 | 50% |
Last updated: 31 January 2024
Dates & times
- May
Mode of delivery On Campus (Parkville) Contact hours Total time commitment 170 hours Teaching period 29 May 2023 to 21 July 2023 Last self-enrol date 8 June 2023 Census date 16 June 2023 Last date to withdraw without fail 7 July 2023 Assessment period ends 28 July 2023
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
Last updated: 31 January 2024