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Causal Inference (MAST90140)

Graduate courseworkPoints: 12.5Online

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Overview

Availability
Semester 1 - Online
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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 can 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

Code Name Teaching period Credit Points
MAST90102 Foundations of Regression
July (Dual-Delivery - Parkville)
 12.5
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

DescriptionTimingPercentage

A 10-20-minute recorded presentation in which causal concepts are to be discussed

  • 1000 words (equivalent)
From Week 4 to Week 620%

Theoretical and practical exercises, and a written report, that allows demonstration of the understanding of concepts, the application of these concepts and interpretation of results

  • 1500 words
From Week 7 to Week 930%

Theoretical and practical exercises

  • 1000 words
From Week 10 to Week 1220%

A written report covering a practical problem that allows demonstration of the understanding of concepts, the application of these concepts and interpretation of results. Presentation of results is in a format suitable for a journal publication.

  • 1500 words
From Week 12 to Week 1430%

Last updated: 31 January 2024

Dates & times

  • Semester 1 - Online
    CoordinatorJohn Carlin
    Mode of deliveryOnline
    Contact hours
    Total time commitment170 hours
    Teaching period26 February 2024 to 26 May 2024
    Last self-enrol date 8 March 2024
    Census date 3 April 2024
    Last date to withdraw without fail 3 May 2024
    Assessment period ends21 June 2024

    Semester 1 contact information

    jbcarlin@unimelb.edu.au

    Melbourne School of Population and Global Health

    OR

    Currently enrolled students:

    Future Students:



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.

  • 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