What makes a satisfying life? Prediction and interpretation with machine-learning algorithms
Andrew E. Clark, Conchita D'Ambrosio, Niccolo Gentile and Alexandre Tkatchenko
Machine Learning (ML) methods are increasingly being used across a variety of fields and have led to the discovery of intricate relationships between variables. We here apply ML methods to predict and interpret life satisfaction using data from the UK British Cohort Study. We discuss the application of first Penalized Linear Models and then one non-linear method, Random Forests. We present two key model-agnostic interpretative tools for the latter method: Permutation Importance and Shapley Values. With a parsimonious set of explanatory variables, neither Penalized Linear Models nor Random Forests produce major improvements over the standard Non-penalized Linear Model. However, once we consider a richer set of controls these methods do produce a non-negligible improvement in predictive accuracy. Although marital status, and emotional health continue to be the most important predictors of life satisfaction, as in the existing literature, gender becomes insignificant in the non-linear analysis.
25 March 2025
Review of Income and Wealth 71(2) 2025
https://onlinelibrary.wiley.com/doi/full/10.1111/roiw.70003
This Journal article is published under the centre's Community Wellbeing programme.