Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains such as climate science, finance, and healthcare. Given the complexity of real-world relationships and the nature of observations in discrete time,
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.