Chemotherapy, including neoadjuvant chemotherapy administered prior to surgery, is a fundamental treatment strategy for breast cancer, yet patient responses are highly variable. Current chemotherapy planning primarily depends on a limited set of clinical and pathological factors, which often neglects the complex molecular heterogeneity inherent in individual tumours. Existing predictive models, while valuable, generally fail to address the essential clinical question: Which patients truly benefit from chemotherapy, and how should drug combinations be optimally tailored to individual patients? In response to this critical gap, we propose CTR (Causality-based Therapy Recommendation), a novel causal recommendation framework designed to personalise chemotherapy decisions in breast cancer treatment. CTR integrates causal inference methods, specifically causal trees, to estimate heterogeneous treatment effects and provide recommendations regarding whether chemotherapy would benefit individual patients, particularly in terms of achieving pathological complete response (pCR) and improved survival outcomes. Moreover, CTR extends its application by optimising combinations of chemotherapy agents, such as taxanes, anthracyclines, and anti-HER2 therapies, to enhance treatment efficacy. Evaluation of CTR on the DUKE and TransNEO datasets demonstrates significant improvements over existing state-of-the-art methods in terms of improved survival and recovery rates. CTR thus represents a promising step towards personalised precision oncology by enabling clinicians to make informed, data-driven chemotherapy decisions.