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Title Causal Inference for Epidemiological Research
Course number 2416
Programme Epidemiologi
Language English
Credits 1.5
Date 2014-03-10 -- 2014-03-14
Responsible KI department Institutionen för medicinsk epidemiologi och biostatistik
Specific entry requirements The students are expected to have taken Epidemiology I, Epidemiology II, Biostatistics I, and Biostatistics II. Exceptions can be made if the students have taken other courses with an equivalent content.
Intended learning outcomes 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 Marginal Structural Models to analyze longitudinal data, with additional help from a skilled statistician
Contents of the course Causal inference from observational data is a key task of biostatistics 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 biostatistical concepts and methods can be understood within this general framework. The course emphasizes conceptualization 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.
Teaching and learning activities Lectures and group discussions.
Compulsory elements
Examination There will be a take-home exam handed out at the last day of the course. Students who fail will be given the opportunity to write at a maximum 2 re-exams. Dates for the re-exams will be announced later.
Literature and other teaching material - ""Causal Inference"", by Miguel Hernan and James Robins. Unpublished, but partly avaliable on Miguel's homepage http://www.hsph.harvard.edu/faculty/miguel-hernan/causal-inference-book/ - Slides to be handed out during the course.
Number of students 12 - 25
Selection of students Eligible doctoral students will be prioritized according to 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) date for registration as a doctoral student (priority given to earlier registration date). To be considered, submit a completed application form. Give all information requested, including a short description of current research and motivation for attending, as well as an account of previous courses taken.
More information Prerequisite knowledge equivalent to ""Epidemiology I: Introduction to epidemiology"" (course 1577), ""Epidemiology II: Design of epidemiological studies"" (course 1622), ""Biostatistics I: Introduction for epidemiologists"" (course 1579) and ""Biostatistics II: Logistic regression for epidemiologists"" (course 1513) or courses with corresponding learning outcomes.
Additional course leader
Earlier evaluation of the course Evaluation report
Course responsible Yudi Pawitan
Institutionen för medicinsk epidemiologi och biostatistik

Yudi.Pawitan@ki.se
Contact person Gunilla Nilsson Roos
Institutionen för medicinsk epidemiologi och biostatistik
08-524 822 93
gunilla.nilsson.roos@ki.se