Go to AAAI 2026 Workshop AI4ES homepageEcological Determinants of Antidepressants Prescriptions in England: Using Machine Learning for Causal Discovery
Keywords: Environmental exposures, Causal discovery, Large language Model, Machine Learning, Graph analysis
Abstract: Ecological studies on depression have explored the complex associations between environmental, social, and economic factors and mental health outcomes, but they often fall short of answering causal questions, which can limit the effectiveness of the interventions. To demonstrate the potential of machine learning in uncovering the causal mechanisms behind antidepressant prescriptions, which are overwhelmingly used to treat depressive disorders, we systematically examined 28,640 small geographical areas in England, each labeled with 27 socio-demographic and environmental indicators, as well as with the total annual per capita antidepressant prescriptions. Specifically, we employed a novel approach that integrates statistical analysis, machine learning, and domain expertise. Our results highlight the pivotal roles of ethnicity, green spaces, and dense urban structure as indirect causal links shaping antidepressant prescriptions, potentially mediated by hidden variables such as cultural attitudes and the likelihood of experiencing depressive symptoms. To validate our findings, we compared them with previous research, statistical modeling of ecological data, and results obtained through querying Large Language Models about causal links. Our causal inference approach showed efficacy in determining information flow directions and unveiling subtle relationships by considering a web of causation. Specifically, the results aligned for the most part with existing research, such as the complex associations with employment and economic conditions. Moreover, the findings also brought up some connections that warrant further research with individual-level data, including different effects from tree cover versus NDVI greenery.
Submission Number: 7
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