Prediction under intervention: challenges and trade-offs
Exploring how causal intervention can be used to make ‘predictions under intervention’ where decisions are made on the basis of the predication, with an example in cardiovascular risk.
Causality and prediction are often two separate activities. In particular, prediction can be done in a way that is agnostic to underlying knowledge, mechanism or causal structure. However, it is very often useful to exploit any existing causal knowledge in the context of prediction.
This is most directly the case when a decision is to be made on the basis of a prediction, where the decision will affect the risk itself (sometimes called performative prediction). In such cases, decisions are better supported by information about how the predicted risk reacts to those decisions: prediction under intervention.
In this talk, I will describe our attempts to build models that allow for prediction under intervention, and the inevitable challenges and trade-offs that arise. The motivating example is our recent development of a model that predicts cardiovascular risk under intervention, which is designed to be used to support decisions in primary prevention.
Speakers
Matthew Sperrin, University of Manchester
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