## 2 Background and Relevant Research
### Structural Causal Models  
One approach to the analysis of observational data, including RWD, is the use of Structural Causal Models (SCMs) [23]. SCMs are mathematical models that answer questions of causality and conceptualize the hidden underlying \"causal story\" of a dataset. SCMs consist of two sets of variables, the exogenous variables \(U\), whose values are determined by external factors, and the endogenous variables \(V\), whose values are determined by the model. Moreover, SCMs include a set of functions \(f\) that assigns each variable in \(V\) a value, based on the values of the other variables in the model [24]. SCMs correspond to directed acyclic graphs (DAGs), where each node of the SCM represents a variable and each edge connecting two nodes represents a causal relationship between the variables. Using a SCM, a practitioner or a researcher can translate how and which features for a specific case interact with each other.  
SCMs have been previously applied for the analysis of observational data in various disciplines. For example, [PERSON] and [PERSON] [25] used SCMs as a framework to overcome the limitations of inferring causality from observational data in the field of ecology. They highlighted that the use of statistical analysis of observational data leads to biased estimation of causal effects and instead proposed the use of DAGs to represent the causal structure of each problem. By applying graphical rules, such as the backdoor and frontdoor criteria [23], researchers can determine the necessary statistical adjustments for establishing causal relationships from observational data. Finally, they present simulated examples in which they demonstrate how the use of these criteria can provide accurate causal estimates in the field of ecology, without relying on randomized experiments.  
[PERSON] et al. [26] presented the application of SCMs in precision medicine. In particular, they highlighted the necessity of answering causal questions (e.g., what is the response of a patient when different treatments are issued) and mentioned that SCMs can be leveraged for causal reasoning. The authors focused on the development of a SCM that captures the interactions between demographic information, disease covariates and magnetic resonance (MR) images of the brain in patients with multiple sclerosis. Using the SCM, the authors generated counterfactual images depicting how an MR image of the brain would appear if certain demographic or disease factors were altered. These counterfactualimages have applications in modeling disease progression and can be utilized in downstream image processing tasks where it is necessary to control for confounding variables.  
[PERSON] and [PERSON] [27] highlighted the importance of asking causal questions in the field of epidemiology and underlined the potential benefits of using formal frameworks for causal inference. In particular, the significance of causality lies in the fact that we can identify and intervene with the best way in the root cause of something, rather than simply spotting a pattern or a behavior. Following this reasoning, the authors focused on using SCMs, since their flexibility enables them to state valid assumptions and, thus, lead to the development of a causal model which can describe the true data-generating process. Overall, they aimed to emphasize the utility of causal thinking when conducting a statistical analysis, to clarify the capabilities and limitations of SCMs as well as to provide an overview of SCMs as a powerful tool offered to be used in the field of epidemiology. Finally, it is clearly stated that the judicious use of a causal framework can significantly enhance the quality of epidemiological research and improve research in various disciplines relying on statistics to understand how the world functions.