Open Seminar with Xu Zong on Population Health, Ageing, and Machine Learning

Seminar title: Machine learning for social determinants of health: From identifying predictors to estimating heterogeneous and causal effects

Xu Zong is a Postdoctoral Researcher at the Helsinki Institute of Demography and Population Health, University of Helsinki, where he also completed his PhD. His research focuses on population health, ageing, and machine learning. His work has been published in journals including Social Science & Medicine, featured in international media such as The Telegraph.

Background information on Zong´s talk:

Drawing on a series of recent studies, this talk illustrates three complementary ways in which machine learning can advance population health research. First, supervised and explainable methods, such as tree-based ensemble models combined with SHAP-based interpretation, can identify the most important determinants of health outcomes. Second, methods such as causal forests can move beyond average effects to uncover heterogeneity, revealing how these associations vary across countries and population subgroups. Third, approaches such as double (debiased) machine learning can help strengthen causal inference by flexibly adjusting for high-dimensional confounders while preserving valid statistical estimation. At the seminar, our guest speaker will illustrate when and how each of these tools can strengthen public health and social science research, and also discuss their limitations.

The seminar is arranged by the Department of Public Health Sciences, Stockholm University.