Multivariate Time Series Causal Foundational Model with In-Context Learning

Predicting how a dynamic system evolves under a sequence of interventions is central to healthcare, economics, and engineering, and is complicated by time-varying confounding when treatments depend on history. Classical methods are trained from scratch per dataset and predict a single scalar outcome. Prior-data fitted networks (PFNs) enable training-free inference, but temporal PFNs either lack interventions or predict only one target variable, ignoring how the other covariates respond to treatment. We introduce MTS-CFM, a multivariate time series causal foundational model that forecasts every variable of the system, both the outcome and all covariates, over a multi-step horizon under a planned treatment sequence. Given an individual's history, a context set of related trajectories, and the treatment plan, it predicts the entire horizon in one forward pass without fine-tuning. MTS-CFM is trained entirely on synthetic trajectories sampled from a prior over temporal structural causal models with explicit treatment assignment. Without task-specific training, it performs competitively with domain-trained baselines and a causal PFN on interventional outcome prediction, while forecasting time-varying covariates more accurately than comparable baselines. These results make in-context learning a practical route to causal forecasting under dynamic treatments.