Causal inference in depression: understanding beyond correlation

Abstract

Depression remains one of the most pressing mental health concerns worldwide, intensified further by the socioeconomic and psychological impacts of the COVID-19 pandemic. Understanding the underlying mechanisms that contribute to depressive symptoms has therefore become a major research priority. While traditional statistical and machine learning models have been effective in identifying associations between risk factors and depression, they often fail to distinguish correlation from causation. Explainable Artificial Intelligence (XAI) methods, such as SHAP and LIME, have improved transparency by revealing which features influence model predictions; however, they remain fundamentally correlational and do not provide insight into the true causal pathways that drive depressive outcomes. To address this limitation, this research integrates machine learning, explainable AI, and causal inference to explore the causal factors behind depression among the Bangladeshi population during the COVID-19 pandemic. Using XGBoost for predictive modeling, the study first evaluates the relative importance of features through gain-based measures and SHAP value interpretation. Subsequently, a causal inference framework is constructed following Judea Pearl’s principles to identify and estimate direct causal effects using the backdoor adjustment method with a generalized linear model estimator. Finally, a combined feature-selection pipeline is developed that retains causally significant variables and iteratively removes weakly correlated ones to test their joint predictive strength. The results reveal that while several factors exhibit high correlation and feature importance in black-box models, only a subset demonstrates genuine causal influence on depressive outcomes. This distinction underscores the importance of causal reasoning in mental health analytics. Overall, the study establishes that integrating causal inference within predictive frameworks not only enhances interpretability and trustworthiness but also provides a clearer understanding of which factors can truly influence and potentially mitigate depression.