Ice crystals in clouds aggregate, shaping snow and rain, yet rates are hard to measure. Using cloud seeding, we sampled crystals downwind after known times. A deep-learning algorithm quantified aggregation by counting crystal components. Initial ice concentration was the main driver, confirmed by causal analysis, physics, and machine learning, though weaker than theory predicts. Temperature, size, and shape also mattered, while turbulence was negligible.