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Researching causal relationships in time series data using Temporal Convolutional Networks (TCNs) combined with attention mechanisms. This approach aims to identify complex temporal interactions. Additionally, we're incorporating uncertainty quantification to enhance the reliability of our causal predictions.
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python
Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.