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Causal Inference (MAST90140)
Graduate courseworkPoints: 12.5Online
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
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
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
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: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
POPH90148 | Probability and Distribution Theory |
Semester 1 (Online)
Semester 2 (Online)
|
12.5 |
OR
Code
Name
Teaching period
Credit Points
MAST90102
Linear Regression
12.5
Code
Name
Teaching period
Credit Points
POPH90144
Linear & Logistic Regression
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: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
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: 3 November 2022
Dates & times
- Semester 2 - Online
Principal coordinator Lyle Gurrin Mode of delivery Online Contact hours Total time commitment 144 hours Teaching period 3 August 2020 to 1 November 2020 Last self-enrol date 14 August 2020 Census date 21 September 2020 Last date to withdraw without fail 16 October 2020 Assessment period ends 27 November 2020 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: 3 November 2022
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: 3 November 2022