Causal Inference (UoA)

Postgraduate | 2026

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Area/Catalogue
STAT 5017
Course ID icon
Course ID
208184
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
5
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No
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Note:
Course data is interim and subject to change

Course overview

This course covers modern statistical methods for assessing the causal effect of a treatment or exposure from randomised or observational studies. Content includes: Explanation of the fundamental concept of counterfactual or potential outcomes and an introduction to causal diagrams (or directed acyclic graphs (DAGs)) to visually identify 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.

Course learning outcomes

  • See Study Guides at: https://url.au.m.mimecastprotect.com/s/jyzMCmO5QMCjMl1XJtJi1URPVES?domain=bca.edu.au/

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A