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Unit of study_
BSTA5017: Causal Inference (CSI)
2024 unit information
This unit covers modern statistical methods that are now available for assessing the causal effect of a treatment or exposure from a randomised or observational study. The unit begins by explaining the fundamental concept of counterfactuals or potential outcomes and introduces causal diagrams (or directed acyclic graphs) 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 with conventional statistical methods. Emphasis will be placed on interpretation of results and understanding the assumptions required to allow inferences to be called causal. Stata and R software will be used to apply the methods to real datasets.
Unit details and rules
Managing faculty or University school:
Public Health
| Code | BSTA5017 |
|---|---|
| Academic unit | Public Health |
| Credit points | 6 |
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Prerequisites:
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BSTA5210 or BSTA5211 or (BSTA5007 and BSTA5008) |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
- LO1. Use counterfactuals (potential outcomes) to precisely define causal effects
- LO2. Describe the differences between association and causation, and the fundamental assumptions required for causation
- LO3. Construct causal diagrams and use them to identify potential sources of bias
- LO4. Implement causal inference methods, using software, for single time point and longitudinal exposures, and for mediation analyses
- LO5. Interpret results of analyses in light of the causal assumptions required
- LO6. Effectively communicate results of causal analyses in language suitable for a clinical or epidemiological journal
Unit availability
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Modes of attendance (MoA)
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