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Abstract: Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., ...

Abstract: Causality is the relationship where one event contributes to the production of another, with the cause being partly responsible for the effect and the effect partly dependent on the cause.

Official implementation of the paper "Jacobian Regularizer-based Neural Granger Causality". Remark 1: you can also run the demo.py directly. The details of hyperparameters can be seen in appendix.

We introduce a framework that integrates Causal Representation Learning (CRL) with Language Models (LLMs) to enable causally-aware reasoning and planning in interactive environments. Our approach ...