Matthew Sperrin, Manchester, United Kingdom
"When Is Causal Thinking Needed for Prediction?"
"When prediction models are implemented, they are often interpreted causally. For example, a model that predicts mortality in hospitalized covid-19 patients might be used to triage patients - with patients at low risk being considered for discharge. However, a patient may be predicted as low risk because of the care that similar patients received in hospital in the past - which would not be equivalent to the care that they would receive post-discharge. In this talk, I will describe some of the problems with misinterpretation of prediction models - and introduce some of the emerging methods to develop 'counterfactual' prediction models."

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