series event

An introduction to counterfactual prediction and conformal inference

There is an increased interest in prediction under hypothetical future exposures, often with a view of informing medical decision making. For example, we may want to build a clinical prediction model for the risk of developing a health outcome, such as heart failure, if a patient were to start pharmacological treatment. Building such a prediction model using observational data is complicated, due to “confounding by indication” and “treatment drop-ins”.  

In the first talk, we will motivate the need for adopting a causal framework when constructing such counterfactual (“what-if”) prediction models, while in the second half we will introduce conformal inference, as a way to construct prediction intervals around our counterfactual predictions.  

The talk on the 17th of March can be thought of as a primer (or gentle introduction) to the topic, before the research-focus talk on the 24th of March on using conformal prediction for counterfactuals and individual treatment effects.  


Please note that the time listed is Greenwich Mean Time (GMT)


Follow webinar link. Free and open to all.