Understanding is often defined as the ability to form mental models of the world, reason about cause and effect, and predict ...

However, we did not include age as a covariate, as it did not correlate with any of the ... Nonetheless, given the hypothesis’s prediction of a causal link between these variables, we further examined ...

In this paper, we propose a Memory-Aware Graph Interactive Causal Network (MagicNet) that considers both temporal and spatial dependencies in financial documents and introduces causality-based ...

Gray matter volume (GMV) of each participant was extracted using voxel-based morphometry, a group-level structural covariance network (SCN) was constructed based on the GMV of each participant, and ...

Using genotype data from individuals living today, we are interested in identifying human population structure and how it correlates with covariates such as language ... including heterogeneity in ...

For causal graphs we propose a definition of proper time which for small scales is based on the concept of volume, while for large scales the usual definition of length is applied. The scale where the ...

Causal-learn (documentation, paper) is a python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and ...