In fields such as finance, climate science, and neuroscience, inferring
causal relationships from time series data poses a formidable challenge. While
contemporary techniques can handle nonlinear relationships be
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.