Undergraduate Honors Thesis Presentation: Cross-National Predictive and Causal Analysis of Depression Risk Factors in Asian Student and Working Populations
Purpose: Prior research has established perceived pressure and life satisfaction as important correlates of depression, yet their causal interplay remains insufficiently identified. This study aims to disentangle whether satisfaction acts as an independent protective factor or operates by buffering pressure, and to identify population-specific risk profiles across students and workers.
Methods: We applied a causal machine learning framework to harmonized data from India, China, and Malaysia (total N=28,243). We integrated random forests and logistic regression with Causal Mediation Analysis and Causal Forests. To explore theoretical ambiguity regarding directionality, we benchmarked the causal pathway between pressure and satisfaction using numerical simulation benchmarks.
Results: Pressure emerged as the dominant predictor across all cohorts. Simulation-based benchmarking suggested that the observed data were more consistent with a causal pathway flowing from Life Satisfaction → Pressure → Depression than with the reverse hypothesis. Satisfaction mitigated depression partially through pressure reduction (proportion mediated ≈ 15.1%), rather than functioning exclusively as a direct mechanism. A distinct developmental reversal was observed: younger age predicted vulnerability in students, whereas older age predicted risk in workers. Causal forests further revealed that the depressogenic impact of pressure was significantly amplified in students with high anxiety.
Conclusion: Pressure appears to function as a proximal bottleneck for depression risk, while life satisfaction may act as an antecedent buffer. These findings challenge uniform risk models by highlighting age-related context dependence and suggest that precision interventions targeting stress reduction and high-anxiety subgroups offer the most effective pathway for breaking the causal cycle of depression.
Thesis Advisor: Ran Chen