Explainable machine learning for sequences of demographic statuses

Abstract

The article presents a case study on demographic sequences analysis through modern machine learning (ML) techniques. The studied data contains demographic and socioeconomic events, where the events are presented as sequences of statuses. The involved demographers are interested in applications of advanced ML techniques and interpretable patterns for their needs. We show how Shapley value-based explanations can be obtained for such sequential data with powerful ML approach, namely gradient boosting over decision trees. Thus, it helps to understand the critical and influencing events for a particular individual life-course sequence and explain predictions.