Exploring potential disease risk factors is critical for precision medicine, enabling the identification of biomarkers and supporting accurate diagnostic tools and interventions. However, traditional statistical and machine learning methods often fail to reduce disease risk effectively, as they focus on correlations while ignoring essential causal relationships. Causality, on the other hand, could identify causal relationships and predict effects from large-scale observational data, discovering underlying data generation mechanisms. Therefore, we propose a novel causality driven analysis framework (CDAF) to identify causal risk factors from observational data. CDAF employs a converging causality growth algorithm to discover essential causal relationships through Bayesian scoring criterion, modeled as a causal graph. It estimates the average treatment effect (ATE) of direct causal edges using causal inference, ranking the importance of risk factors. CDAF demonstrates excellent performance on both benchmark datasets and other real clinical datasets. We empirically demonstrate that our framework offers a powerful tool for uncovering complex disease mechanisms, advancing precision medicine, and enabling personalized treatment strategies.