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Prediction algorithms are often used to help decision making, for instance to evaluate whether the prognosis of an individual patient warrants a certain medical treatment. However, a major limitation is that most prediction algorithms ignore the role that treatments already played in the data on which they were developed and evaluated. In some situations using standard prediction models for decision support will actually do more harm than good. To effectively use prediction models in decision making, they must focus on causal (or counterfactual) predictions, where for each individual we predict what their outcome would be under the treatment options under consideration, also referred to as prediction under interventions. In this presentation, I will give an overview of estimands for prediction under interventions and the causal assumptions needed to estimate these from observational data. I will illustrate how existing causal inference methods can be adapted to provide predictions under interventions and discuss new metrics for assessing their performance.

Nan van Geloven is an assistant professor in Biostatistics working at the department of Biomedical Data Sciences at the Leiden University Medical Center in the Netherlands. After obtaining a MSc in Mathematics at Delft University of Technology and a PhD in Biostatistics at the University of Amsterdam, she is currently leading a research line on causal inference methods in survival analysis in Leiden. She introduced the predictimand framework: a general framework that spells out how to handle post-baseline treatments in clinical prediction models. She serves in the Executive Committee of the International Society for Clinical Biostatistics and is active member of the international STRATOS Causal Inference Topic Group which provides guidance on causal inference from observational data.

This seminar will be held via Zoom and will be recorded.

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