Introduction to Causal inference
Graduate School of Health and Medical Sciences at University of Copenhagen
This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH.
Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list.
The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee.
Aim
This six-day intensive course aimed at Ph.D. students in Biostatistics, Epidemiology, Health Data Science, or Statistics who would like an introduction to causal inference and in particular statistical methods for causal inference. When participating in this course, you will get a working knowledge of the conceptual roots of causal inference, as well as hands on experience using the most common methods used in causal inference in observational and clinical trials data.
Learning objectives
A student who has met the objectives of the course will be able to:
1. Be aware of and being able to distinguish between the differing schools of thought in causal inference.
2. Understand the basic principles of defining and identifying a causal estimand.
3. Be able to display information about your assumptions via a DAG or SWIG
4. Understand and know how to perform a basic IV analysis, and a basic mediation analysis
5. Be aware of the different types of sensitivity analysis, including nonparametric causal bounds
6. Perform and present the results of a basic causal analysis in a meaningful and convincing manner, that conveys clear causal reasoning.
7. Understand and present a novel statistical method for causal inference using the concepts and basic methods you have learned about in the course
Content
Day 1: Causal language, counterfactuals, DAGs and other causal structures
Day 2: Identification and basic estimation
Day 3: Point estimation ATE, g-computation, g-estimation, IPW and double robust
Day 4: Mediation and longitudinal analysis
Day 5: IV analysis, partial identification, bounds and sensitivity analysis
Day 6: Student presentations
Statistical software
We will be working with the open source statistical software R using the interface RStudio. To participate in the course you must bring your own laptop with R and RStudio installed.
Prerequisites
Familiarity with R programming is necessary for taking part in the exercise classes and for completing the homework problems. If you are not familiar with R programming, we recommend that you complete the free access e-learning course at http://r.sund.ku.dk/ before starting on this course.
Statistical methods, some basic statistical background will be assumed, such as a basic understanding of regression analysis.
Mathematical theory, some basic understanding of calculus.
Participants
Ph.D.-students. In case of vacant seats also other medical researchers. Max. 36 participants.
Relevance to graduate programmes
The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:
All graduate programmes
Language
English
Form
Lectures and interactive learning for 4 hours and computer exercises for 2 hours
Course director
Associate professor Erin Gabriel, Section of Biostatistics
Teachers
Associate professor Erin Gabriel
Postdoctoral fellow Marie Breum
Dates
Mondays and Thursdays: 5 (Tuesday), 7, 11, 14, 18, 25 November 2024, all days 10-17
Course location
CSS
Registration:
Please register before 10 October 2024
Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment.
Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.