Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease study

ArXi:2604.23465v1 Announce Type: new Single-arm trials accelerate study timelines by reducing the number of patients that must be recruited for a concurrent control group. However, these designs require an alternative comparator to estimate treatment effects. One approach is to construct a virtual control arm using a machine learning (ML) model trained on external control data to predict the counterfactual outcomes of the treatment arm.