Overview
This course develops a conceptual and applied foundation in causal inference for health research. Students examine counterfactual and potential-outcomes frameworks, causal diagrams, exchangeability, positivity, consistency, treatment versions, estimands, and causal effects, while distinguishing descriptive, predictive, and causal questions.
The course addresses confounding, mediation, effect modification, selection bias, measurement error, collider bias, time-varying exposures and confounders, missing data, transportability, and generalizability. Students apply randomization, restriction, matching, stratification, standardization, inverse probability weighting, regression adjustment, propensity scores, g-methods, instrumental variables, regression discontinuity, difference-in-differences, interrupted time series, and target trial emulation at an introductory applied level.
Through critical appraisal, graphical analysis, statistical practice, and research proposal development, students formulate answerable causal questions, identify assumptions, select appropriate designs and analyses, evaluate threats to validity, and communicate uncertainty without overstating conclusions.
Learning Outcomes
- Define causal estimands using counterfactual and potential-outcomes frameworks.
- Construct and interpret directed acyclic graphs to represent causal structures and identify sources of bias.
- Evaluate exchangeability, positivity, consistency, treatment versions, and other assumptions underlying causal claims.
- Distinguish association, prediction, and intervention effects in randomized and observational health research.
- Select appropriate identification strategies and analytic methods for confounding, mediation, effect modification, missing data, and time-varying exposures.
- Apply standardization, regression adjustment, propensity scores, inverse probability weighting, and introductory g-methods to health data.
- Assess the suitability of instrumental variables, regression discontinuity, difference-in-differences, interrupted time series, and target trial emulation designs.
- Critique published causal studies with respect to design, measurement, analysis, assumptions, internal validity, transportability, and generalizability.
- Synthesize evidence from a simulated or public dataset to estimate and interpret a clearly defined causal effect.
- Develop and justify a causal research proposal that addresses a substantive health question and communicates uncertainty appropriately.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Critical appraisal of a published causal health study | 15.00% |
| Assignment | DAG-based bias analysis | 15.00% |
| Deliverable | Causal analysis of a simulated or public dataset | 25.00% |
| Capstone | Causal research proposal | 25.00% |
| Exam | Final examination | 20.00% |
Prerequisites
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.
