Causal Inference: Transfer Entropy + CCM + Causal Emergence

What does it mean for one signal to drive another — and at what scale does the causation live? Three lenses, each built for a regime the others miss. Transfer entropy (histogram, KSG k-nearest-neighbour, and symbolic estimators) measures directed information flow in noisy, stochastic systems. Convergent cross-mapping recovers coupling direction in deterministic, weakly-coupled dynamics — exactly where correlation and Granger causality fail — through Takens delay embedding. Causal emergence (ΔEI over all 256 elementary cellular automata) turns to the orthogonal question of scale: when a coarse-grained description carries more causal power than the microdynamics beneath it. A shared benchmark shows where the three agree and where each sees something the others cannot. Built in Julia as an interactive Pluto notebook.