What makes a satisfying life? Prediction and interpretation with machine-learning algorithms

Clark, Andrew E., D'Ambrosio, Conchita, Gentile, Niccoló and Tkatchenko, Alexandre (2022) What makes a satisfying life? Prediction and interpretation with machine-learning algorithms. CEP Discussion Papers (1853). London School of Economics and Political Science. Centre for Economic Performance, London, UK.

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Abstract

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.

Item Type:	Monograph (Discussion Paper)
Official URL:	https://cep.lse.ac.uk/_new/publications/discussion...
Additional Information:	© 2022 The Authors
Divisions:	Centre for Economic Performance
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HC Economic History and Conditions
JEL classification:	I - Health, Education, and Welfare > I3 - Welfare and Poverty > I31 - General Welfare; Basic Needs; Living Standards; Quality of Life; Happiness C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C63 - Computational Techniques
Date Deposited:	13 Jan 2023 13:51
Last Modified:	11 Dec 2024 19:43
URI:	http://eprints.lse.ac.uk/id/eprint/117887

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