Generating Counterfactual Patient Timelines from Real-World Data

ArXi:2604.02337v1 Announce Type: new Counterfactual simulation - exploring hypothetical consequences under alternative clinical scenarios - holds promise for transformative applications such as personalized medicine and in silico trials. However, it remains challenging due to methodological limitations. Here, we show that an autoregressive generative model trained on real-world data from over 300,000 patients and 400M patient timeline entries can generate clinically plausible counterfactual trajectories.