How do interpretable internal features that implement a learned capability emerge during training? We present a mechanistic interpretability study of 3-digit addition in a 19M-parameter transformer trained from scratch, using the grokking transition as a natural microscope for feature genesis. Our framework, Causal Genesis Attribution (CGA), combines checkpoint-resolved training dynamics, Sparse Autoencoder (SAE) feature analysis, and counterfactual training interventions to trace arithmetic and carry features from emergence to causal function. We find layer-4 SAE features that are silent on non-arithmetic text, selective for the arithmetic probes, and co-emerge with validation accuracy at step 500 (near-zero to 85%). Zero-ablating the top arithmetic features drops exact-match accuracy from 100% to 0%, showing that these directions are necessary for the learned addition algorithm. A four-arm intervention study reveals what sustains these features: removing arithmetic data collapses feature activation by 81%; matched numeric-noise replacement also fails, ruling out surface token statistics; and poisoning answers by +137 hyperactivates features by 27% while making the model produce systematically shifted outputs. Across four seeds, the genesis detector fires consistently, with detection accuracy stable at 85.6% ± 1.0%. Together, these results provide a mechanistic data–feature–capability chain for an emergent arithmetic reasoning ability.