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 resolve theoretical ambiguity regarding directionality, we validated the causal pathway between pressure and satisfaction using numerical simulation benchmarks. Results: Pressure emerged as the dominant predictor across all cohorts. Simulation-validated analysis confirmed a causal pathway flowing from Life Satisfaction -> Pressure -> Depression, rejecting the reverse hypothesis. Satisfaction mitigated depression partially through pressure reduction (proportion mediated around 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 acts as the proximal bottleneck for depression risk, while life satisfaction functions 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.

翻译：目的：先前研究已证实感知压力和生活满意度是抑郁的重要相关因素，但其因果交互作用尚未得到充分阐明。本研究旨在厘清满意度是作为独立的保护因子，还是通过缓冲压力发挥作用，并识别学生与工作者群体中特定人群的风险特征。方法：我们采用因果机器学习框架，对来自印度、中国和马来西亚的标准化数据（总样本量 N=28,243）进行分析。该方法整合了随机森林与逻辑回归，并结合因果中介分析与因果森林模型。为澄清方向性上的理论模糊性，我们通过数值模拟基准验证了压力与满意度之间的因果路径。结果：压力在所有队列中均显现为主导预测因子。经模拟验证的分析确认了“生活满意度 -> 压力 -> 抑郁”这一因果路径，否定了反向假设。满意度缓解抑郁的作用部分通过压力降低实现（中介比例约为15.1%），而非仅作为直接机制运作。研究观察到显著的发展阶段逆转现象：年轻年龄预测学生群体的脆弱性，而年长年龄则预测工作者群体的风险。因果森林模型进一步揭示，压力对抑郁的诱发效应在高焦虑学生群体中被显著放大。结论：压力是抑郁风险的近端瓶颈因素，而生活满意度则作为前因缓冲因子发挥作用。这些发现通过强调年龄相关的环境依赖性，挑战了统一的风险模型，并表明针对压力减轻和高焦虑亚群的精准干预，为打破抑郁的因果循环提供了最有效的路径。