Understanding the determinants of well-being: a case study in interpretable machine learning
Video recording:
Speaker: Niccolo Gentile (Faculty of Humanities, Education and Social Sciences; University of Luxembourg)
Title: Understanding the determinants of well-being: a case study in interpretable machine learning
Time: Wednesday, 2022.01.19, 10:00 a.m. (CET)
Place: fully virtual (contact Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
Abstract: A key objective of empirical research in subjective well-being is the understanding of the relationship between the features and the target, in particular in terms of marginal effect. This necessity explains why, to this day, linear methods remain the mainstream in this field of research. In this presentation, we explore the application of novel model-agnostic interpretable tools in nonlinear contexts, in particular with reference to novel techniques like SHAP Feature Importances in Random Forests, including a comparison with other methods like Feature and Permutation Importance. Differences, advantages and disadvantages of each method are presented in the context of understanding of the determinants of subjective well-being.