# Delving into Causal Discovery in Health-Related Quality of Life Questionnaires
## 1 Introduction
Regression models have also been used to assess the correlation of HRQoL scores with various participants' characteristics (e.g., [18; 19; 21; 24; 27; 30]). An aspect that has been so far inadequately explored in the HRQoL-related literature is causal discovery. While standard statistical correlation measures the strength and direction of a relationship between two variables, it does not necessarily imply a cause-and-effect relationship, i.e., that one of the variables is the cause of the other variable, which is considered to be the effect. Correlation does not distinguish between whether changes in one variable cause changes in another, whether both variables are influenced by a third variable, or whether the observed association is coincidental. Causal discovery goes beyond correlation analysis, aiming to infer causal relationships between variables [38]. It is a tool with wide potential that may aid to validate and/or reveal new knowledge in diverse fields (see, e.g., [39; 40; 41; 42; 43; 44; 45; 46]). Common methods in causal discovery include causal Bayesian networks (BNs), which are graphical models representing and describing the causal relations between random variables through a directed acyclic graph (DAG), and structural equation modeling (SEM), which investigates the relationships between constructs relative to a certain phenomenon [47]. The efficient detection, assessment, and in-depth interpretation of the cause-and-effect relationships among specific HRQoL questionnaire items related to physical, psychological, lifestyle, and social parameters could offer valuable insights into patient care and management. This critical perspective motivated the current study. More specifically, un derstanding the causal relationships between these parameters could validate and reinforce current knowledge and/or reveal hidden aspects, thus enabling the design and implementation of more targeted strategies in patient management. For instance, if worrying about future health issues is the cause and feeling depressed is the effect, taking action to address these worries might aid to alleviate depression. A potential strategy could be to educate patients about their disease to better understand the diverse implications. Assessing the structure of the causal relationships among the questionnaire items could additionally result in understanding which are the most critical items. Namely, a specific item/node that is revealed to be the cause of several other items (thus exhibiting a high degree of centrality) stands as an important parameter to address in priority. In the previous example, if worrying about future health issues is, additionally, the cause of irritation, sleep deterioration, social alienation, etc., then it could be deducted that addressing these worries is a priority, and efforts and means should be dedicated primarily in this direction. A few efforts have been made within the literature to examine causal relationships in HRQoL questionnaires. For instance, [PERSON] et al. (2018) used SEM to investigate the causal relationships among bio-physiological status, social support, symptoms, functional status, general health perception, and HRQoL to Thai patients with heart failure. Structural equation modeling was also employed to examine the causal relationships between age, antiretroviral treatment, social support, symptom experience, self-care strategies, and HRQoL of people living with HIV/AIDS in the northern region of Thailand ([PERSON] et al., 2018). [PERSON] et al. (2018), on the other hand, developed a causal diagram of sport participation for children and adolescents with heart disease based on the use of an HRQoL questionnaire ([PERSON] et al., 2018; [PERSON] et al., 2018) by employing the Greedy Fast Causal Inference technique for continuous variables, which is a BN-based structure learning algorithm. However, the authors mentioned a limitation concerning the dataset used for identifying the DAG, since this study was based on 121 patients, which is a small sample size.