Drug synergy is crucial for developing effective combination therapies, but traditional screening methods suffer from inefficiency and high costs. While deep learning shows promise for predicting drug synergy, current approaches using Transformers and graph neural networks focus on combining drug and cell line features without modelling how genes causally influence drug responses. To address this limitation, we propose CADS (Causal Adjustment for Drug Synergy), a deep learning framework that integrates causal relationships between genes and drug responses. Leveraging multi-omics data, CADS uses a learnable mask mechanism to identify key causal genes while filtering out irrelevant genetic factors through backdoor adjustment—a causal inference technique. Our model achieves two key objectives simultaneously: accurate prediction of drug synergy and interpretable causal gene discovery. Experiments on multiple datasets show that CADS consistently outperforms state-of-the-art methods across multiple metrics. Case studies demonstrate that CADS can reduce unnecessary complexity while providing more biological insights through its gene importance scores, which help identify clinically validated cancer-related genes that mediate drug interactions. Taken together, CADS advances combination therapy prediction by explicitly modelling drug synergy causal genes, offering enhanced interpretability for AI-based drug development.