An Awesome List of the latest time series papers and code from top AI venues.

This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).

Recursive causal discovery with Julia

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

Implementation PyTorch codes for causal discovery

Library of causal analisys alorthims which was created as main subject of BSc thesis. It was further developted as part of MA thesis. It implements various implementations of Granger Analisys algorithms with modifications. It is also designed to allow easy customization and development.