In this paper we discuss and apply machine learning techniques, using ideas from a core research area in the artificial intelligence literature to analyse simultaneously timing, sequencing, and quantum of life course events from a comparative perspective. We outline the need for techniques which allows the adoption of a holistic approach to life course analysis, illustrating the specific case of the transition to adulthood. We briefly introduce machine learning algorithms to build decision trees and rule sets and then apply such algorithms to delineate the key features which distinguish Austrian and Italian pathways to adulthood, using Fertility and Family Survey data. The key role of sequencing and synchronization between events emerges clearly from the analysis.
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