Transfer Entropy & Network Inference

Transfer entropy T(X→Y) measures directed information flow: how much knowing X's past reduces uncertainty about Y's future, beyond what Y's past alone tells us. It detects asymmetric causal influence without assuming linearity. The true network has 5 nodes with known directed edges. TE inference recovers the network from time series data — accuracy improves with sample size N and coupling strength κ.