Combination therapy is critical in cancer treatment, yet accurately identifying synergistic drug combinations and their mechanisms remains challenging due to poor interpretability and limited generalization in existing models. Here, we introduce PISynergy, a novel synergy prediction framework that integrates precise synergy classification with causal interpretability. PISynergy consists of: (1) a triple interaction attention (TIA)-based prediction module explicitly modeling drug–drug–cell line interactions, strengthened by cross-layer residual connections for enhanced stability; and (2) a causal interpreter employing encoder-decoder architecture with causal constraints to uncover key drug substructures and gene features underpinning synergistic interactions. Evaluations on two public datasets demonstrated PISynergy significantly outperformed existing methods with improvements of up to 13% in core metrics and substantially reduced prediction variance. In challenging cold-start scenarios, it exhibited remarkable generalization. Ablation studies confirmed both TIA and residual connections as essential for accuracy and robustness. Importantly, the causal interpreter revealed biologically meaningful substructures and validated pathways. Additionally, literature-supported novel synergistic pairs emerged from top-ranked predictions. Thus, PISynergy provides an accurate, generalizable, and causally interpretable platform for drug synergy prediction, facilitating trustworthy insights and experimental validation of novel therapeutic combinations.