What is it about?
In this study, we examined more than 13,000 adults in the United States aged 50 and older and followed them for eight years. At the start, participants reported how satisfied they felt with their lives overall, and we then tracked whether they were still alive at the end of the follow-up period. Using a modern data-analysis method from machine learning that can detect differences across individuals, we found that higher life satisfaction was linked to a modest survival advantage—on average, about three and a half extra months of life over the eight years. However, the benefits were not the same for everyone: the protective link between life satisfaction and longevity was stronger for people with less money, lower education, poorer health, or from racial minority groups.
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Why is it important?
Major global organizations such as the Organisation for Economic Co-operation and Development, the World Health Organization, and the United Nations are urging countries to look beyond economic growth and also track well-being indicators like life satisfaction when making policy decisions. Many governments have begun doing this, so it is important to understand what potential improvements we might expect from policies that successfully improve life satisfaction—and who is most likely to benefit. Although more research is needed, our results suggest that policies and programs that help people feel more satisfied with their lives could be especially valuable for older adults who face the greatest disadvantages, and may help narrow long-standing gaps in how long different groups live.
Perspectives
One aspect of this study I’m especially excited about is the causal machine-learning approach, a newer type of data analysis that helps reveal how effects differ across groups of people. I hope other researchers will experiment with this approach in the areas they care most about. For those interested in learning more, I’ve been particularly inspired by the work of my co-authors Dr. Koichiro Shiba and Mr. Toshiaki Komura, who have applied this method in creative and rigorous ways.
Eric Kim
University of British Columbia
Read the Original
This page is a summary of: Estimating the heterogeneous effect of life satisfaction on mortality: A causal machine-learning approach., Health Psychology, December 2025, American Psychological Association (APA),
DOI: 10.1037/hea0001569.
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