CIIG Seminar: Vanessa Didelez

Abstract:

In this talk I will review and discuss the gap(s) between what is often thought to be a "causal mediation" research question and the actual (formal) concepts of causal mediation, in particular the notions of so-called natural (in)direct effects. These notions are controversial: not only do they rely on unusual assumptions, that are much more subtle than simply assuming ‘no unmeasured confounding’, these notions of causal effects themselves can be criticized as not providing any practically relevant / actionable insights. Practical alternatives are based on joint or controlled direct effects, or are in terms of sequential / dynamic treatments; another recent proposal are separable (in)direct effects, which are sometimes useful as I will briefly illustrate. The talk will focus on examples, concepts and interpretations rather than technical details.

About the speaker:

Vanessa Didelez is Professor of Statistics and Causal Inference at the Leibniz-Institute of Prevention Research and Epidemiology – BIPS, Bremen, Germany, in a joint appointment with the Department of Mathematics and Computer Science of the University of Bremen. She graduated in the subject of Statistics with Psychology and received her PhD in Statistics from the University of Dortmund, Germany. She spent 16 years in the UK, first at the Department of Statistical Science, University College London, and then at the Department of Mathematics at the University of Bristol. She was appointed at BIPS in 2016, where she is Deputy Head of the Department of Statistical Methods in Epidemiology. In 2025 she was invited to give the annual LSHTM/RSS Perspectives on Statistics in Medicine Lecture on "It’s about time - asking better questions for causal inference with time-dependent data". In 2026 she was awarded the "Suffrage Science Award" by the University of Oxford. Her research combines many aspects of causal inference, graphical modelling, and time-structured / time-to-event data with applications in epidemiology.

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Host or Publisher Causal Inference Interest Group (CIIG)