## 6 Discussion and Conclusions  
In this work, we propose a data analysis workflow, which is developed in the context of the REBECCA project. The main objective is to present a methodology on how to handle RWD and provide accurate and reliable estimations of causal effects for research on the quality of life of BCPs. The proposed methodology consists of the stages of data collection, data curation (including data imputation and outlier detection), model development, treatment effect estimation and finally, the presentation of results. Issues related to data quality as well as sampling, confounding and other biases have an impact on the accuracy of treatment effect estimation. To demonstrate and evaluate the proposed approach, we developed a causal DAG for the case of osteopenia and osteoporosis, which is a comorbidity of AI treatment in postmenopausal BCPs. Specifically, we used the DAG and an associated SCM to generate synthetic datasets and then we applied the proposed workflow to several use case scenarios, including cases with missing values as well as sampling and confounding biases. Based on the results of the experimental analysis, the proposed approach achieves significantly higher estimation accuracy, compared to direct computation of the ATE, in cases where bias is present. This implies that the use of ML models for causal inference is beneficial, since they provide lower estimation errors. The experimental analysis results suggest that there is promise in using automated methods for data quality control and causal effect estimation for studying factors affecting the quality of life of BCPs. The synthetic data used in this study were carefully designed based on established relationships from the literature on clinical trials, as well as input from domain experts (see Appendixes A and B). This approach helped in ensuring that the generated data faithfully represent realistic clinical scenarios and the underlying mechanisms involved. Nevertheless, even though the generated data may not capture some of the complexity and the nuances of real patient data, they highlight the challenges that arise when attempting to estimate treatment effects on the quality of life of BCPs. Generally, synthetic data offer substantial benefits in addressing the lack of real-world datasets, especially in cases such as the inherent absence of the counterfactual outcomes [69]. However, the synthetic data generation process carries the risk of introducing biases, either from the assumptions made in defining relationships or from the algorithm used to generate the data. To the best of our knowledge, there are no available causal inference datasets for studying the comorbidity of osteopenia and osteoporosis; hence, an evaluation for ensuring that key statistical properties of the RWD are preserved by the generated synthetic is impossible. This can be considered as a limitation of this approach, since the generated synthetic data may not fully capture the complexity of RWD. An elegant approach for addressing this difficulty will certainly be included in our future research. In this research, we focused our attention on the estimation of the ATE and CATE, which constitute quantitative measures of how treatments perform on average and for specific subgroups. It is worth highlighting the importance of translating the findings made from the estimation of these metrics into hypotheses for future research as well as clinical practice for supporting clinicians in making more informed, patient-specific treatment decisions. However, this is not possible due to the lack of available real-world data. In our future work, we intend to seek and obtain real BC patient data from clinical trials for assessing the quality of the generated synthetic data. In addition, we will focus our attention on developing methods that combine the data-driven causal effect estimation models with Structural Causal Models, as well as methods for model validation and testing.