The human nervous system is one of the most complicated systems in nature. Complex
nonlinear behaviours have been shown from the single neuron level to the system level. For …

Recently, causal feature selection (CFS) has attracted considerable attention due to its
outstanding interpretability and predictability performance. Such a method primarily includes …

Causal Inference plays an significant role in explaining the decisions taken by statistical
models and artificial intelligence models. Of late, this field started attracting the attention of …

While Granger causality (GC) has been often employed in network neuroscience, most GC
applications are based on linear multivariate autoregressive (MVAR) models. However, real …

Feature selection is a crucial preprocessing step in data analysis and machine learning.
Since causal relationships imply the underlying mechanism of a system, causality-based …

Conditional Granger causality, based on functional magnetic resonance imaging (fMRI) time
series signals, is the quantification of how strongly brain activity in a certain source brain …

An important goal in neuroscience is to elucidate the causal relationships between the
brain's different regions. This can help reveal the brain's functional circuitry and diagnose …

While Granger Causality (GC)-based approaches have been widely employed in a vast
number of problems in network science, the vast majority of GC applications are based on …

Granger causality method analyzes the time series causalities without building a complex
causality graph. However, the traditional Granger causality method assumes that the …

Extracting causal mechanisms from observational data represents a paradigm shift for
machine learning, unlocking more robust generalization capabilities. This quantitative study …