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This article investigates causal relationships leading to emergence in an agent-based model of human behaviour. A new method based on nonlinear structural causality is formulated and practically demonstrated. The method is based on the concept of a causal partition of a model variable which quantifies the contribution of various factors to its numerical value. Causal partitions make it possible to judge the relative importance of contributing factors over crucial early periods in which the emergent behaviour of a system begins to form. They can also serve as the predictors of emergence. The time-evolution of their predictive power and its distribution among their components hint at the deeper causes of emergence and the possibilities to control it.

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