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1Ghent University, Belgium; 2University of Cambridge, United Kingdom
Evaluating treatment effects using observational data or trials with complex intercurrent events may require accounting for high-dimensional confounding. This course will describe how, in these challenging situations, machine learning and variable selection procedures can be used to infer causal effects. The first part is a high level overview of how and why this methodology works, touching on recent developments in ‘double machine learning’ and ‘targeted maximum likelihood estimation’. In the second part the participants will exercise the concepts and methods explained during the first part via the analysis of real data sets.
This introductory course is aimed at researchers in the (pharmaceutical) industry and academia working with observational as well as trial data; a basic understanding of causal inference can be helpful but is not necessary. We foresee a mix of lectures and hands-on exercises using R.
