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Causal Inference (MAST90140)
Graduate courseworkPoints: 12.5Online
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About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: MSPGH Website
- Email: Enquiry Form
Overview
Availability | Semester 2 - Online |
---|---|
Fees | Look up fees |
This unit covers modern statistical methods for assessing the causal effect of a treatment or exposure from randomised or observational studies. The unit begins by explaining the fundamental concept of counterfactual or potential outcomes and introduces causal diagrams and directed acyclic graphs (DAGs) to identify visually confounding, selection and other biases that prevent unbiased estimation of causal effects. Key issues in defining causal effects that are able to be estimated in a range of contexts are presented using the concept of the “target trial” to clarify exactly what the analysis seeks to estimate. A range of statistical methods for analysing data to produce estimates of causal effects are then introduced. Propensity score and related methods for estimating the causal effect of a single time point exposure are presented, together with extensions to longitudinal data with multiple exposure measurements, and methods to assess whether the effect of an exposure on an outcome is mediated by one or more intermediate variables. Comparisons will be made throughout with “conventional” statistical methods. Emphasis will be placed on interpretation of results and understanding the assumptions required to allow causal conclusions. Stata and R software will be used to apply the methods to real study datasets.
Intended learning outcomes
On completion of this subject, students should be able to:
- Use counterfactuals (potential outcomes) to define precisely causal effects;
- Describe the differences between association and causation, and the fundamental assumptions required for causation;
- Construct causal diagrams and use them to identify potential sources of bias;
- Implement causal inference methods, using software, for single time point and longitudinal exposures, and for mediation analyses;
- Interpret results of analyses in light of the causal assumptions required;
- Effectively communicate results of causal analyses in language suitable for a clinical or epidemiological journal.
Generic skills
- Independent problem solving;
- facility with abstract reasoning;
- clarity of written expression;
- sound communication of technical concepts.
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90100 | Probability & Inference in Biostatistics | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
and ONE of the following:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90102 | Foundations of Regression | July (Dual-Delivery - Parkville) |
12.5 |
Code | Name | Teaching period | Credit Points |
---|---|---|---|
POPH90144 | Regression Methods in Health Research | July (Dual-Delivery - 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 |
---|---|---|
Short answers to module exercises
| Week 4 | 10% |
Short answers to module exercises
| Week 6 | 10% |
Report – Providing a causal perspective on results from an existing observational study and defining the "target trial".
| Week 8 | 30% |
Short answers to module exercises
| Week 10 | 10% |
Short answers to module exercises
| Week 12 | 10% |
Report – statistical analysis of data from a cohort study with sections on research question, methods, results and discussion/conclusion.
| During the examination period | 30% |
Last updated: 31 January 2024
Dates & times
- Semester 2 - Online
Coordinator Koen Simons Mode of delivery Online Contact hours Total time commitment 144 hours Teaching period 25 July 2022 to 23 October 2022 Last self-enrol date 5 August 2022 Census date 31 August 2022 Last date to withdraw without fail 23 September 2022 Assessment period ends 18 November 2022 Semester 2 contact information
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: MSPGH Website
- Email: Enquiry Form
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Subject notes
This subject is delivered online via our partners in the Biostatistics Collaboration of Australia (www.bca.edu.au). It is not generally available in the Master of Public Health nor in any program outside the MSPGH.
Last updated: 31 January 2024