A foundation model is a machine learning model trained on a large and diverse
set of data, typically using self-supervised learning-based pre-training
techniques, that can be adapted to various downstream tasks.
Causal Pretraining explores supervised learning to discover causal relationships from time series data, demonstrating that performance increases with data and model size and suggesting the potential for a foundation model for causal discovery.