Causal Model Research Articles

EXPRESS: On the interchangeability of presentation order for cause and effect: Experimental tests of cue and outcome density effects.

Studies of cue-outcome contingency learning demonstrate outcome-density effects: participants typically overestimate contingencies when the outcome event is relatively frequent. Equivalent cue-density effects occur, though these have been examined less often. Few studies have simultaneously examined both those event density effects or have manipulated the presentation order of the events,limiting knowledge of whether these phenomena share underlying principles-. We report three well-powered experiments to address those gaps. Participants judged the effectiveness of a medical treatment after viewing a series of pairings for two events, a cause (treatment given vs. not) and an effect (patient recovered vs. not). Experiment 1 manipulated both event densities independently. We then manipulated the presentation order for the cause and the effect, alongside a manipulation of effect density (Experiment 2a) or cause density (Experiment 2b). Experiment 1 found a large main effect of event-density (η_p^2 =.55), which was qualified by a significant interaction between event type and density level (η_p^2 =.10) whereby effect density had greater impact than cause density. Experiments 2a and 2b found effects for effect-density (η_p^2=.60) and cause-density (η_p^2=.31). The effects of cause-effect presentation order were always small and non-significant. We conclude that effect-density manipulations had substantial impact on contingency judgments, and cause-density manipulations less so. Moreover, it matters little which event (cause or effect) is seen first. These findings have implications for contingency, associative, probabilistic, and causal models of contingency judgment; primarily, that people may be more sensitive to the causal status of events than to their temporal order of presentation.

Associations of various healthy dietary patterns with biological age acceleration and the mediating role of gut microbiota: results from the China Multi-Ethnic Cohort study.

To investigate the associations between dietary patterns and biological aging, identify the most recommended dietary pattern for coping with biological aging and explore the potential mediating role of gut microbiota in less-developed ethnic minority regions (LEMRs). This prospective cohort study included 8288 participants aged 30-79 years from the China Multi-Ethnic Cohort study (CMEC). Anthropometric measurements and clinical biomarkers were utilized to construct biological age based on Klemera and Doubal's method (KDM-BA) and KDM-BA acceleration (KDM-AA). Dietary information was obtained through the baseline food frequency questionnaire (FFQ). Six dietary patterns were constructed: plant-based diet index (PDI), healthful plant-based diet index (hPDI), unhealthful plant-based diet index (uPDI), healthy diet score (HDS), Dietary Approaches to Stop Hypertension (DASH), and alternative Mediterranean diets (aMED). Follow-up adjusted for baseline analysis were employed to assess the associations between dietary patterns and KDM-AA. Additionally, quantile G-computation was utilized to evaluate the significant beneficial and harmful food groups. In the subsample of 764 participants with gut microbiota data obtained through 16S rRNA gene sequencing, we used causal mediation model to explore the mediating role of gut microbiota in the associations between dietary patterns and KDM-AA. The results showed that all dietary patterns were associated with KDM-AA. Transitioning from non-compliance to compliance, DASH exhibited the strongest negative association with KDM-AA [β = -0.91, 95%CI (-1.19, -0.63)]. The component analyses revealed that tea and soybean products were the significant beneficial food groups, while salt, preserved vegetables, red and processed meats were identified as the major harmful food groups. In mediation analysis, the decreased abundance of Synergistetes phylum and Pyramidobacter genus possibly mediated the negative associations between plant-based diets and KDM-AA (5.61%-9.19%). Overall, healthy dietary patterns, especially DASH, are negatively associated with biological aging in LEMRs. The Synergistetes and Pyramidobacter may mediate the associations between plant-based diets and biological aging. Developing appropriate strategies may promote healthy aging in LEMRs.

On the exchangeability property in causal models

On the exchangeability property in causal models

Metabolomic profiling identifies signatures and biomarkers linking air pollution to dementia risk: a prospective cohort study

Exposure to air pollution has been associated with increased dementia. However, it remains unknown what specific metabolic mechanisms play a role in this relationship. We included 192,300 dementia-free participants from the UK Biobank cohort study. Annual concentrations of air pollution were assessed based on the residential address. Elastic net regression was performed to identify air pollution-related metabolites, and metabolic score was constructed. Cox regression models and covariate balancing generalized propensity scores (CBGPS) regression models were conducted to explore the longitudinal associations between air pollution/metabolic signatures and dementia risk. The underlying mechanisms between air pollution and dementia driven by metabolic signature or specific metabolites were also investigated. A total of 2,592 incident dementia cases were documented. We identified the metabolite profiles in response to air pollution exposure, including 87 metabolites for PM2.5, 65 metabolites for PM10, 76 metabolites for NO2, and 71 metabolites for NOx. The air pollution-related metabolic signatures were associated with increased risk of dementia, with hazard ratios (HR) of 1.17 (95% CI: 1.12, 1.22), 1.06 (95% CI: 1.02, 1.11), 1.16 (95% CI: 1.10, 1.21), and 1.17 (95% CI: 1.12, 1.22) for PM2.5, PM10, NO2 and NOx, respectively. The associations persisted using causal models. Metabolic signatures mediated the associations between air pollution exposure and dementia risk, with mediation proportions ranging from 6.57% to 12.71%. Additionally, we observed that a metabolite known as free cholesterol in medium VLDL (M-VLDL-FC) played a crucial mediating role. Our study provides novel insights into the metabolic mechanisms linking air pollution exposure to dementia risk.

Body ownership alterations in stroke emerge from reduced proprioceptive precision and damage to the frontoparietal network.

Stroke patients often experience alterations in their subjective feeling of ownership for the affected limb, which can hinder motor function and interfere with rehabilitation. In this study, we aimed at disentangling the complex relationship between sensory impairment, body ownership (BO), and motor control in stroke patients. We recruited 20 stroke patients with unilateral upper limb sensory deficits and 35 age-matched controls. Participants performed a virtual reality reaching task with a varying displacement between their real unseen hand and a visible virtual hand. We measured reaching errors and subjective ownership ratings as indicators of hand ownership. Reaching errors were modeled using a probabilistic causal inference model, in which ownership for the virtual hand is inferred from the level of congruency between visual and proprioceptive inputs and used to weigh the amount of visual adjustment to reaching movements. Stroke patients were more likely to experience ownership over an incongruent virtual hand and integrate it into their motor plans. The model explained this tendency in terms of a decreased capability of detecting visuo-proprioceptive incongruences, proportionally to the amount of proprioceptive deficit. Lesion analysis further revealed that BO alterations, not fully explained by the proprioceptive deficit, are linked to frontoparietal network damage, suggesting a disruption in higher-level multisensory integration functions. Collectively, our results show that BO alterations in stroke patients can be quantitatively predicted and explained in a computational framework as the result of sensory loss and higher-level multisensory integration deficits. Swiss National Science Foundation (163951).

The analysis of a causal model of learning organization on sustainable organizational performance of public IT companies in China: Knowledge management practices and innovation capability as multiple mediators

This research aims to empirically examine the role of learning organization practices in enhancing sustainable organizational performance, utilizing knowledge management and innovation capability as mediating variables. The study was conducted in public IT companies across China, which is a vital sector for driving innovation and economic growth. A mixed-methods approach was employed, with quantitative methods accounting for 70% and qualitative methods for 30% of the research. Purposive sampling was utilized to distribute questionnaires to 546 employees from 10 public IT companies. Statistical analysis was conducted using Structural Equation Modeling (SEM). The findings indicate that learning organization practices significantly influence knowledge management practices (β = 0.785, p < 0.001) and innovation capability (β = 0.405, p < 0.001). Furthermore, knowledge management practices positively contribute to sustainable organizational performance (β = 0.541, p < 0.001), while innovation capability also has a positive effect (β = 0.143, p < 0.001). Moreover, knowledge management practices partially mediate the relationship between learning organization practices and sustainable performance, with a total effect of 0.788 (p < 0.001). The mediating role of innovation capability is also significant, with a total effect of 0.422 (p = 0.045). The study further includes qualitative in-depth interviews with 20 managers from 10 IT companies across five regions in China: East, South, West, North, and Central. Senior managers were selected through a stratified sampling method to ensure comprehensive representation by including both the largest and smallest companies in each region. These findings underscore the critical role of learning organizations in promoting sustainability through effective knowledge management and innovation capabilities within the IT sector.

The Impact of Carbon Awareness, Country-Governance, and Innovation on the Cost of Equity: Evidence from Oil and Gas Firms

The present paper explores the impact of carbon awareness on the cost of equity (COE) with a keen focus on the influence of country governance and innovation factors. Drawing on Bloomberg data spanning from 2011 to 2022, our analysis encompasses 263 firms within the Oil and Gas sector across 50 countries, culminating in 2,122 annual observations. Through the employment of a comprehensive suite of regression analyses—including median regression, M-estimator regression, and MM-estimator regression, alongside Firm-Fixed Effect and Reverse Causality Models—we uncover a consistently negative association between carbon awareness and COE. Our investigation further assesses the effect of country-level governance on COE, revealing a universally negative correlation. Among governance indicators, regulatory quality emerges as a particularly significant factor. Similarly, we observe a consistent negative relationship between country-level innovation and COE, reinforcing the significance of national innovation capabilities in financial performance metrics. These findings remain robust across a range of tests, underscoring their reliability and relevance. The study is important for policymakers, investors, managers, and strategic environmentalists.

Global analysis of acoustic frequency characteristics in birds.

Animal communication plays a crucial role in biology, yet the wide variability in vocalizations is not fully understood. Previous studies in birds have been limited in taxonomic and analytical breadth. Here, we analyse an extensive dataset of >140 000 recordings of vocalizations from 8450 bird species, representing nearly every avian order and family, under a structural causal model framework, to explore the influence of eco-evolutionary traits on acoustic frequency characteristics. We find that body mass, beak size, habitat associations and geography influence acoustic frequency characteristics, with varying degrees of interaction with song acquisition type. We find no evidence for the influence of vegetation density, sexual dimorphism, range size and competition on our measures of acoustic frequency characteristics. Our results, built on decades of researchers' empirical observations collected across the globe, provide a new breadth of evidence about how eco-evolutionary processes shape bird communication.

COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys

Understanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour during an infectious disease outbreak can help to protect vulnerable populations and guide equity-driven interventions. The COVID-19 pandemic probably exerted different stresses on individuals in different sociodemographic groups and ensuring fair access to and usage of COVID-19 tests was a crucial element of England's testing programme. We aimed to investigate the relationship between sociodemographic factors and COVID-19 testing behaviours in England during the COVID-19 pandemic. We did a population-based study of COVID-19 testing behaviours with mass COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from Oct 1, 2020, to March 30, 2022. We used mass testing data for lateral flow device (LFD; data for approximately 290 million tests performed and reported) and PCR (data for approximately 107 million tests performed and returned from the laboratory) tests made available for the general public and provided by date and self-reported age and ethnicity at the lower tier local authority (LTLA) level. We also used publicly available data on mean population size estimates for individual LTLAs, and data on ethnic groups, age groups, and deprivation indices for LTLAs. We did not have access to REACT-1 or ONS-CIS prevalence data disaggregated by sex or gender. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by both self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. With confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability subsequent to reporting a positive LFD for PCR tests by sociodemographic groups. We also estimated the daily incidence, allowing us to calculate the fraction of cases captured by the testing programme. From March, 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per capita as individuals in the least deprived areas (median ratio 0·50 [IQR 0·44-0·54]). During the period October, 2020, to June, 2021, PCR testing patterns showed the opposite trend, with individuals in the most deprived areas performing almost double the number of PCR tests per capita than those in the least deprived areas (1·8 [1·7-1·9]). Infection prevalences in Asian or Asian British individuals were considerably higher than those of other ethnic groups during the alpha (B.1.1.7) and omicron (B.1.1.529) BA.1 waves. Our estimates indicate that the England Pillar 2 COVID-19 testing programme detected 26-40% of all cases (including asymptomatic cases) over the study period with no consistent differences by deprivation levels or ethnic groups. Testing biases for PCR were generally higher than those for LFDs, in line with the general policy of symptomatic and asymptomatic use of these tests. Deprivation and age were associated with testing biases on average; however, the uncertainty intervals overlapped across deprivation levels, although the age-specific patterns were more distinct. We also found that ethnic minorities and older individuals were less likely to use confirmatory PCR tests through most of the pandemic and that delays in reporting a positive LFD test were possibly longer in populations self-reporting as "Black; African; Black British or Caribbean". Differences in testing behaviours across sociodemographic groups might be reflective of the higher costs of self-isolation to vulnerable populations, differences in test accessibility, differences in digital literacy, and differing perceptions about the utility of tests and risks posed by infection. This study shows how mass testing data can be used in conjunction with surveillance surveys to identify gaps in the uptake of public health interventions both at fine-scale levels and across sociodemographic groups. It provides a framework for monitoring local interventions and yields valuable lessons for policy makers in ensuring an equitable response to future pandemics. UK Health Security Agency.

Toward the successful adoption of Lean Six Sigma in manufacturing organizations: proposing a causal model

PurposeThis paper aims to provide new and valuable insights for organizations that have decided to implement Lean Six Sigma (LSS) as a strategy for continuous improvement in search of business excellence. This objective is achieved through statistically modeling the causal relationships between the critical success factors (CSFs) of LSS for a successful deployment of this strategy.Design/methodology/approachA statistically validated questionnaire was used to collect information from LSS practitioners in manufacturing industries located on Mexico’s northern border. Some hypotheses for relationships between LSS CSFs are developed theoretically first and empirically tested later using the structural equation modeling technique.FindingsProposed relationships between LSS CSFs have been demonstrated to be statistically significant, making clear that this implementation should always begin with a strong and decided participation and commitment of top management. Later, other CSFs must be activated and put into operation in a specified order to increase the probability of a successful implementation reflected in well-executed continuous improvement projects and achieving the expected benefits.Originality/valueThis paper proposes a structural model which has been statistically validated with information directly collected from skilled practitioners who have developed continuous improvement projects using the LSS methodology. Further, some relationships between LSS CSFs that, to the best of the author’s knowledge, had not been demonstrated empirically are now explored and verified in our work.

The Impact of COVID-19 and Structural Market Changes on the Greek Stock Market: An Empirical Analysis

This study examines the dual impact of COVID-19 and structural market changes on the Greek stock market, specifically the Athens stock exchange (ASE). Quantitative data analysis, granger causality models, and Hsiao’s approach to granger causality were employed to investigate the causalities between COVID-19 pandemic measures and ASE returns. Our findings reveal that pandemic-related variables, including lockdown measures and mobility restrictions, significantly negatively affected the ASE by disrupting economic activities and diminishing investor confidence. Conversely, the study also highlights the positive impact of monetary policy interest rates on the market, which helped stabilize the financial environment by lowering borrowing costs and stimulating investment. These results underscore the intricate dynamics between crisis-induced restrictions and monetary policy adjustments, offering valuable insights for policymakers and investors navigating economic uncertainties.

From Real-World Data to Causally Interpretable Models: A Bayesian Network to Predict Cardiovascular Diseases in Adolescents and Young Adults with Breast Cancer.

Background: In the last decades, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic role of cancer therapies, making cardiovascular diseases (CVDs) among the most frequent, although rare, long-term sequalae. Leveraging innovative artificial intelligence (AI) tools and real-world data (RWD), we aimed to develop a causally interpretable model to identify young BC survivors at risk of developing CVDs. Methods: We designed and trained a Bayesian network (BN), an AI model, making use of expert knowledge and data from population-based (1036 patients) and clinical (339 patient) cohorts of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009-2019. The performance achieved by the BN model was validated against standard classification metrics, and two clinical applications were proposed. Results: The model showed a very good classification performance and a clear causal semantic. According to the predictions made by the model, focusing on the 25% of AYA BC survivors at higher risk of developing CVDs, we could identify 81% of the patients who would actually develop it. Moreover, a desktop-based app was implemented to calculate the individual patient's risk. Conclusions: In this study, we developed the first causal model for predicting the CVD risk in AYA survivors of BC, also proposing an innovative AI approach that could be useful for all researchers dealing with RWD. The model could be pivotal for clinicians who aim to plan personalized follow-up strategies for AYA BC survivors.

Sustainability assessment with the green retrofitting concept for building: A study case in the Gresik regency, Indonesia

In green construction, sustainable resources are essential. One such material is copper, which is widely utilized in electronics, transportation, manufacturing, and residential buildings. As a very useful material, it has many beneficial impacts on human life. Observed from the recent demand spike is in line with the overall trend and the current growing smelter construction in Indonesia. Researchers intend to adapt the existing Copper Smelting Plant Building into an environmentally friendly building as a part of the production chain, in addition to reducing public and environmental concerns about the consequences of this development. We have identified a disparity in cost, where the high cost of green buildings is an obstacle to its implementation to enhance the cost performance with increased renewable energy of the Smelter Construction Building, this study investigates the application of LEED parameters to evaluate green retrofit approaches through system dynamics. The most relevant features of the participant assessments were identified using the SEM-PLS approach, which is used to build and test statistical models of causal models. We have results for this Green Retrofitting study following significant variables according to the following guidelines: innovation, low-emission materials, renewable energy, daylighting, reducing indoor water usage, rainwater management, and access to quality transit.

Research on safety control technology of high-speed railway combined test based on threatening event analysis

PurposeSafety management is a key point and poses a challenge in joint testing. To detect and address potential accidents' hidden dangers early, this paper conducts research on the safety control technology for high-speed railway joint tests by incorporating the concept of hazardous events.Design/methodology/approachAiming at ensuring the safety of high-speed railway combined inspections and trials, this paper starts from the dual prevention mechanism. It introduces the concept of threatening events, defines them and analyzes the differences between threatening events and railway accidents. The paper also proposes a cause model for threatening events in high-speed railway combined inspections and trials, based on three types of hazard sources. Furthermore, it conducts research on the control strategies for these threatening events.FindingsThe research on safety control technology for high-speed railway combined operation and testing, based on the analysis of threatened events, offers a new perspective for safety management in these operations. It also provides theoretical and practical support for the transition from passive prevention to active risk pre-control, which holds significant theoretical and practical value.Originality/valueThe innovation mainly includes the following three aspects: (1) Building on the traditional dual prevention mechanism, which includes risk hierarchical management and control as well as hidden danger investigation and management, a triple prevention mechanism is proposed. This new mechanism adds the management of threatening events as the third line of defense. The aim is to more comprehensively identify and address potential security risks, thereby enhancing the efficiency and effectiveness of security management. (2) In this paper, the definition of a railway threatening event is clarified, and the causative model of a high-speed railway threatening event based on three kinds of danger sources is proposed. (3) This paper puts forward the control strategy of the high-speed railway combined operation and trial, which includes five key links: identification, reporting, analysis, rectification and feedback, which provides a new perspective for the safety management of the high-speed railway combined operation and trial and has important theoretical and application value.

Framework for approaching federated causal inference based on real-world observational data sources

Abstract Context Causal inference methods, generally requiring detailed individual information, allow us to evaluate public health interventions in real-world observational settings. When comparing interventions across borders and for this purpose leveraging distributed sensitive individual-level data, a federated approach can be aided. However, reusing such distributed data while maintaining privacy and interoperability, is a challenging endeavor. Methodological framework In the context of the BY-COVID project, a methodological framework to approach federated causal inference was published in October 2023. It is presented as a set of guidelines in the form several consecutive steps, starting with defining a research question, to building a causal model, translating the causal model into data requirements captured within a Common Data Model (CDM), generating synthetic data complying with the requirements captured in the CDM, and developing interoperable analysis scripts which can be distributed for deployment in different federated nodes. The proposed framework was built through implementing and combining existing methodologies and taking into account the principles of legal, organizational, semantic and technical interoperability. Objectives This presentation will provide an overview of the proposed framework, and its demonstration by performing an international comparison of the real-world effectiveness of SARS-CoV-2 primary vaccination campaigns in preventing infections at different federated sites. Challenges encountered (e.g. difficulties in complying with data requirements by linking heterogeneous local data sources) and lessons learned in the course of the demonstrator will be highlighted. Implications We aim to contribute to a public health research field where the rapid assessment of emerging population-health research questions (potentially feeding into evidence-based policy-making) is achievable, in that way contributing to European pandemic preparedness.

Heterogeneous effect of sodium-glucose cotransporter 2 inhibitors on cardiovascular events among people with type 2 diabetes: an application of machine learning to target trial emulation

Abstract Background Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are widely used to reduce cardiovascular risk in patients with type 2 diabetes. However, certain patient subgroups, including younger individuals or those with lower body mass index (BMI), have been underrepresented in previous randomized clinical trials. Therefore, the consistency of the effectiveness of SGLT2i has not been thoroughly investigated. Purpose This research aims to investigate the individual benefit of SGLT2i on cardiovascular risk reduction across the diverse patient population with type 2 diabetes Methods We investigated the effectiveness of SGLT2i versus active comparators (dipeptidyl peptidase 4 inhibitors [DPP4i]) using a target trial emulation framework, prevalent new-user design, and a nationwide insurer-based database covering more than 30 million working-age citizens in Japan. Participants were identified using data from April 2015 to March 2022 and were followed until March 2023. The primary endpoint was the composite of all-cause death, myocardial infarction, stroke, or heart failure. We then applied the machine learning causal forest algorithm to the emulated trial cohort to predict the individualized benefit of SGLT2i in reducing cardiovascular outcomes. After describing the characteristics of patients with predicted benefits above the median (high-benefit group) and those with predicted benefits below the median (low-benefit group), we compared the long-term survival outcomes between the high-benefit group and the low-benefit group using adjusted Cox proportional hazard models. Results The study included 274,886 participants (SGLT2i, n=137,443; DPP4i, n=137,443) with a mean age of 55.7 years, 17.3% (n=47,517) female, and a mean BMI of 27.3kg/m2. Over a median follow-up of 36 months, 3.6% (n=9,896) experienced the primary outcome. SGLT2i were associated with a reduced incidence of the primary endpoint in the entire cohort (hazard ratio [HR] = 0.94 [95% confidence interval (95%CI): 0.90–0.97]). The causal forest model showed that the effects of SGLT2i were heterogeneous across individuals. Patients in the low-benefit group were characterized by older age, female gender, lower BMI, lower blood pressure, worse kidney function, and milder diabetes status than those in the high-benefit group. While we found the preventive effect of SGLT2i on long-term cardiovascular outcomes in the high-benefit group (HR [95%CI] = 0.86 [0.82–0.90]), the effect was not observed in the low-benefit group (HR [95%CI] = 1.05 [1.00–1.11]; p-for-interaction < 0.001). Conclusion By applying a machine-learning causal forest to a target trial emulation cohort in the general population, we found heterogeneity in the effect of SGLT2i on cardiovascular outcomes, and identified characteristics of patients who are less likely to benefit from SGLT2i. The findings suggest personalized treatment strategies for cardiovascular risk reduction in patients with type 2 diabetes.Graphical summary

Application of artificial intelligence to study the causality in public health: a systematic review

Abstract Introduction In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association. Traditional methodologies often struggle to provide definitive causal conclusions due to confounding variables. However, leveraging artificial Intelligence (AI) and machine Learning (ML) offers a promising solution by modeling complex interactions among relevant factors. This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications. Methods A literature search was conducted in Pubmed, Web of Science, and Scopus, employing the following search strategy: (Causalit*[Title/abstract] OR Causation*[Title/abstract] OR Causal[Title/abstract]) AND (‘Machine Learning’[Title/abstract] OR “Deep Learning” [Title/abstract] OR ‘Artificial Intelligence’ [Title/abstract] OR Algorithm* [Title/abstract]). The search aimed to identify studies encompassing causal inference techniques utilizing ML and AI methodologies in public health. Results From a total of 28,230 articles published up to August 2023, after removing duplicates (11,158) and articles that did not meet the inclusion criteria (16,168 post title/abstract screening, and 856 post full-text screening), the systematic review included 48 articles. Bayesian additive regression trees, Bayesian network models, causal forests analysis, and graphical causal models emerged as the prevailing causal methodologies. AI-driven causal methodologies have been investigated across various domains within public health, encompassing disease prediction, evaluation of treatment efficacy, identification of risk factors, analysis of health behaviors, precision Public Health initiatives, and environmental health assessment. Discussion While our systematic review affirms the potential of AI-driven causal approaches across several public health domains, further research is warranted before these emerging techniques can be effectively integrated into real settings. Key messages • In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association and traditional methodologies often struggle to provide definitive causal conclusions. • This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications.

Navigating the Social Shift: A Preliminary Study of Consumer Behavior on Malaysia's Social Commerce Platforms

The proliferation of online businesses coupled with the advancement of information and communication technologies (ICT) has led to the emergence of social commerce. The integration of social media platforms into e-commerce offers businesses a convenient avenue for engaging with consumers and a novel method for capturing additional sales volume. This seamless integration of social media platforms into current e-commerce practices has significantly increased its success. Research on the factors that affect purchase intentions in social commerce is still sparse, especially in the context of Malaysian consumers. In general, the causal model of purchase intentions on social commerce in this study aims to investigate the impact of the constructs of visibility and metavoicing on purchase intentions among Malaysian millennial consumers. Henceforth, this initial study specifically aims to evaluate the reliability and validity of the IT affordance constructs of visibility and metavoicing through rigorous assessments. At the items-construct level, the internal consistency assessment was conducted using McDonald’s Omega, which is known for its robustness in measuring the reliability of multi-item constructs. At the construct level, discriminant validity and the strength of linear relationships among variables were assessed using Pearson correlation analysis. The pilot dataset gathered was analyzed with SPSS 29, and the results have validated that the predictive model achieved high levels of reliability and validity at both the item and construct levels. Furthermore, the analysis revealed that the independent variables, metavoicing and visibility, have a significant association with purchase intentions, underscoring their importance in understanding consumer behavior.

Servant Leadership of School Heads, Organizational Culture and Teamwork: A Causal Model on Teacher Engagement of Public Secondary School Teachers

This study aims to determine the best-fit structural model for teacher engagement of public secondary school teachers in Region XI, Philippines. It examines the interplay between exogenous variables: servant leadership of school heads, organizational culture, and teamwork on teacher engagement. It tries to understand how these factors influence each other and contribute to fostering a more engaging environment for teachers. The study utilized a descriptive correlational technique through structural equation modeling (SEM). Selected were 400 respondents using stratified random sampling. Findings show that servant leadership of school heads; organizational culture; and teamwork showed significant relationships with the endogenous variable, teacher engagement. Further, results showed that the best-fit model is the Generated Model 3 showing a direct causal relationship of the exogenous and endogenous variables. Furthermore, structure modifications revealed that servant leadership of school heads was grounded by the retained indicators: building community and empowering people which is highly reinforced by organizational culture, described by retained indicators- organizational management and organizational cohesion. Likewise, teamwork was strengthened by the retained indicators: communication and coordination. When the results of this study are given consideration, the goal of quality education in the Sustainable Development Goals that is quality education is positively achieved which resulted from highly engaged teachers.

Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM.

Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a sparse dynamic causal model (sDCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. sDCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range (31 females), providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model predicting the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects (173 females) selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.Significance statement By inferring region-wise hemodynamic profiles at the individual level, this is the first study providing an exhaustive whole-brain characterization of the hemodynamic sensitivity to healthy aging, reporting further evidence of the vascular changes across the adult lifespan. Using a predictive framework, we analysed the statistical influence of advancing age on individual regional hemodynamic attributes, offering a quantitative evaluation of the diverse hemodynamic bias across different brain regions. We then unveiled a specific set of hemodynamic predictors to discriminate young from elderly people, mainly describing vascular properties of right-hemispheric areas. This suggests the asymmetric nature of vascular degeneration processes affecting the human brain at the latest stage of life, other than a potential biomarker that could be relevant for brain-age prediction.