Does Where You Live Affect How You Feel? Causal Evidence from an Integrated Econometric and Machine Learning Framework

Jerry Chen, Li Wan
Proceedings of the 7th Conference on Health, Inference, and Learning, PMLR 333:389-403, 2026.

Abstract

We investigate whether residential relocation causally improves subjective wellbeing by leveraging household relocations in the UK Household Longitudinal Survey as natural experiments. An integrated framework combining a difference-in-differences and synthetic control ensemble with a causal forest model is applied to nearly a decade of panel data. Relocation causes an immediate and sustained improvement of 8% in subjective wellbeing; a change in the built environment type (e.g. suburb to city) adds a further 5%. We demonstrate the complementarity and interoperability of canonical econometric and machine learning methods for causal inference on subjective panel data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v333-chen26b, title = {Does Where You Live Affect How You Feel? Causal Evidence from an Integrated Econometric and Machine Learning Framework}, author = {Chen, Jerry and Wan, Li}, booktitle = {Proceedings of the 7th Conference on Health, Inference, and Learning}, pages = {389--403}, year = {2026}, editor = {Healey, Elizabeth and Fries, Jason and Pollard, Tom and Tang, Shengpu and Zink, Anna and Hartvigsen, Tom and Agrawal, Monica and Finlayson, Sam and Glicksberg, Benjamin and Beaulieu-Jones, Brett and Wang, Kai and Fontalvo, Daseyra and Sarker, Tasmie and Chen, Irene and Alsentzer, Emily}, volume = {333}, series = {Proceedings of Machine Learning Research}, month = {29--30 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v333/main/assets/chen26b/chen26b.pdf}, url = {https://proceedings.mlr.press/v333/chen26b.html}, abstract = {We investigate whether residential relocation causally improves subjective wellbeing by leveraging household relocations in the UK Household Longitudinal Survey as natural experiments. An integrated framework combining a difference-in-differences and synthetic control ensemble with a causal forest model is applied to nearly a decade of panel data. Relocation causes an immediate and sustained improvement of 8% in subjective wellbeing; a change in the built environment type (e.g. suburb to city) adds a further 5%. We demonstrate the complementarity and interoperability of canonical econometric and machine learning methods for causal inference on subjective panel data.} }
Endnote
%0 Conference Paper %T Does Where You Live Affect How You Feel? Causal Evidence from an Integrated Econometric and Machine Learning Framework %A Jerry Chen %A Li Wan %B Proceedings of the 7th Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2026 %E Elizabeth Healey %E Jason Fries %E Tom Pollard %E Shengpu Tang %E Anna Zink %E Tom Hartvigsen %E Monica Agrawal %E Sam Finlayson %E Benjamin Glicksberg %E Brett Beaulieu-Jones %E Kai Wang %E Daseyra Fontalvo %E Tasmie Sarker %E Irene Chen %E Emily Alsentzer %F pmlr-v333-chen26b %I PMLR %P 389--403 %U https://proceedings.mlr.press/v333/chen26b.html %V 333 %X We investigate whether residential relocation causally improves subjective wellbeing by leveraging household relocations in the UK Household Longitudinal Survey as natural experiments. An integrated framework combining a difference-in-differences and synthetic control ensemble with a causal forest model is applied to nearly a decade of panel data. Relocation causes an immediate and sustained improvement of 8% in subjective wellbeing; a change in the built environment type (e.g. suburb to city) adds a further 5%. We demonstrate the complementarity and interoperability of canonical econometric and machine learning methods for causal inference on subjective panel data.
APA
Chen, J. & Wan, L.. (2026). Does Where You Live Affect How You Feel? Causal Evidence from an Integrated Econometric and Machine Learning Framework. Proceedings of the 7th Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 333:389-403 Available from https://proceedings.mlr.press/v333/chen26b.html.

Related Material