This course will give students an overview of causal inference in statistics. First, I will introduce potential outcomes, and show how they can be used to define a causal estimand (with examples). I will then introduce the idea of identification and some key assumptions under which causal estimands can be identified. This will be followed by an introduction to causal diagrams, and show how they can elucidate concepts such as confounding and selection bias. Next, I will describe different approaches to estimating causal effects, including standardisation, inverse probability of treatment weighting (IPW) and modern de-biased machine learning approaches. Finally, time-permitting, we will consider the identification and estimation of effects in the presence of time varying confounding. The concepts will be illustrated using practical exercises and demonstrations in the software R.
practitioners designing and analysing data from experimental and observational studies.
An introductory course in probability and statistics, and familiarity with regression should be sufficient. Familiarity with the statistical software R would be advantageous.
There is no exam associated with this module. If you attend all classes, you will receive a certificate of participation by email at the end of the course.
This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a session on campus.
March 23th from 9 am to 4 pm
Faculty of Science, Campus Sterre, Krijgslaan 281, 9000 Ghent
Building S1
The participation fee is EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations
*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.
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Information on "KMO-portefeuille":https://www.ugent.be/nl/opleidingen/levenslang-leren/kmo
Science Academy
Faculty of Science
science-academy@ugent.be
