Causal inference for epidemiological research
Doctoral course within the doctoral programme in Epidemiology
Course number: 2416
Previous course dates: March 11, 13, 15, 18, 20, 2019
Application period: 15th October - 15th November, 2018
After the course the student will:
- be able to use counterfactuals to express and interpret causal queries
- be able to judge when standard statistical methodology is appropriate for causal inference, and when it is not
- be able to use Directed Acyclic Graphs to describe and analyze complex epidemiological scenarios
- be able to use regression standardization and inverse probability weighting to estimate marginal (population level) causal effects
Content of the course
Causal inference from observational data is a key task of epidemiology and of allied sciences such as sociology, education, behavioral sciences, demography, economics, health services research, etc. These disciplines share a methodological framework for causal inference that has been developed over the last decades.
This course presents this unifying causal theory and shows how epidemiological concepts and methods can be understood within this general framework. The course emphasizes conceptualization, in particular the fundamental difference between association and causation, but also introduces statistical models and methods for time-varying exposures. Specifically, this course strives to a) formally define causal concepts such as causal effect and confounding, b) identify the conditions required to estimate causal effects, and c) use analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. The (causal) methods can be used under less restrictive conditions than the traditional statistical methods. For example, causal methods allow one to estimate the causal effect of a time-varying exposure in the presence of time-dependent confounders that lie on the causal pathway between exposure and outcome.
Literature and other teaching material
- "Causal Inference", by Miguel Hernan and James Robins. Unpublished, but partly available on Miguel's homepage http://www.hsph.harvard.edu/faculty/miguel-hernan/causal-inference-book/
- Slides to be handed out during the course
- Additional scientific papers will be recommended during the course
Group assessments during the course, and an individual written take-home exam at the end of the course.
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