Competing Event Models: Next Event Prediction Under Interventions

Modeling interventions that include timing is an important task in many domains, such as treatments in healthcare, transactions in finance, and others. However, estimating these interventional distributions is challenging because standard next-token predictors, typically implemented using transformers, do not naturally support altering the timing of specific future events. We develop \emph{Competing Event Models} (CEMs), an autoregressive generative approach that simplifies the estimation of interventions on both what interventions are applied and \emph{when}. Drawing from the competing risks literature, CEMs model independent probability distributions for latent times of each event type, where the earliest realized time determines the next event. We show that this framework enables straightforward interventions on event timing and efficient sampling, and, through careful design choices, can also model concurrent events. We evaluate CEMs on a cancer tumor volume simulator and on prediction tasks using the MIMIC-IV dataset. Our results show that CEMs accurately recover causal effects of sequential treatments in simulations while surpassing standard transformer baselines in predictive performance on 3 out of 4 real-world medical data tasks.