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Causal directed acyclic graphs (DAGs) serve as intuitive tools to visually represent causal relationships between variables. While they find widespread use in guiding study design, data collection and statistical analysis, their adoption remains relatively rare in the domain of psychology. In this paper we describe the relevance of DAGs for health psychology, review guidelines for developing causal DAGs, and offer recommendations for their development. A scoping review searching for papers and resources describing guidelines for DAG development was conducted. Information extracted from the eligible papers and resources (n = 11) was categorised, and results were used to formulate recommendations. Most records focused on DAG development for data analysis, with similar steps outlined. However, we found notable variations on how to implement confounding variables (i.e., sequential inclusion versus exclusion). Also, how domain knowledge should be integrated in the development process was scarcely addressed. Only one paper described how to perform a literature search for DAG development. Key recommendations for causal DAG development are provided and discussed using an illustrative example. [ABSTRACT FROM AUTHOR]

AbstractThe introductory statistics course has gotten better over the years, but there are many content areas in STAT 101 that should be reconsidered.

Numerous studies are based on the correlation among stock factors, which affects the measurement value and interpretability of such studies. Research on the causality among stock factors primarily relies on statistical models and machine learning algorithms, thereby failing to fully exploit the formidable computational capabilities of deep learning models. Moreover, the inference of causal relationships largely depends on the Granger causality test, which is not suitable for non-stationary and non-linear stock factors. Also, most existing studies do not consider the impact of confounding variables or further validation of causal relationships. In response to the current research deficiencies, this paper introduces a deep learning-based algorithm aimed at inferring causal relationships between stock closing prices and relevant factors. To achieve this, causal diagrams from the structural causal model (SCM) were integrated into the analysis of stock data. Subsequently, a sliding window strategy combined with Gated Recurrent Units (GRUs) was employed to predict the potential values of closing prices, and a grouped architecture was constructed inspired by the Potential Outcomes Framework (POF) for controlling confounding variables. The architecture was employed to infer causal relationships between closing price and relevant factors through the non-linear Granger causality test. Finally, comparative experimental results demonstrate a marked enhancement in the accuracy and performance of closing price predictions when causal factors were incorporated into the prediction model. This finding not only validates the correctness of the causal inference, but also strengthens the reliability and validity of the proposed methodology. Consequently, this study has significant practical implications for the analysis of causality in financial time series data and the prediction of stock prices. [ABSTRACT FROM AUTHOR]

The introductory statistics course has gotten better over the years, but there are many content areas in STAT 101 that should be reconsidered. [ABSTRACT FROM AUTHOR]

The discipline of systems thinking was developed to study and predict outcomes of large multicomponent systems. It provides a lens to examine complex problems, such as global warming or population health. This article applies the basic tenets of systems thinking to the practice of dentistry, looking specifically at patient safety and quality improvement. Using a systems view of the dental office; an argument is made for the development of a quality initiative through the creation of a mechanism for non-discoverable adverse event reporting. This would create a resource for dentists to learn from past adverse events and prevent them from happening again.

The present investigation, whose main objective is based on modeling the hospital logistics capacity in the Pasco Healthcare Network (Peru) through Systems Dynamics during the first and second wave of the appearance of the SARS-COVid-2 virus, oriented towards a study in the capacity to respond to the most intense health emergency in recent decades. In the first instance, the reality of the studied system was made known, which was possible thanks to the information provided by the actors that make up the hospital logistics capacity, sources uploaded to the WHO worldwide. Afterwards, we proceeded to characterize the variables that make up the system, prioritizing each of these through the application of a structural analysis that measured the levels of influence between one variable and another. Because of this, we proceeded to create the causal diagram, and likewise facilitate the inclusion of the network model through the Stella software. Once the model has been simulated, the results obtained are reported. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and the disclosure of massive judicial data, the LJP task for "smart justice" services has received widespread attention from academia and industry. The task aims to predict the charges, laws, and sentences of a case based on limited factual descriptions of the text. However, existing work lacks research on intelligent decision-making in easily confusing judicial cases, and related models often lack interpretability, which leads to heavy reliance on domain experts for model predictions and hinders the application of LJP in different legal systems. To this end, this article proposes a judicial judgment prediction method CGLJ based on causal graph analysis. Firstly, the causal relationships among elements are mined from unstructured legal fact description texts. Then a causal graph is constructed using a composition method of easily confused accusation clustering. It not only considers the difference among similar fact descriptions, but also enhances the interaction between fact descriptions and laws and regulations. Finally, the constructed causality diagram is integrated into a deep neural network for joint inference to obtain the decision prediction result. In addition, this paper also visualizes the causal diagram inference process in the model prediction, providing better interpretability for the judgment result. The experimental result on the CAIL2018 judicial judgment prediction dataset shows that the proposed method achieves better result than the baseline models. [ABSTRACT FROM AUTHOR]

Thinking about the interactions among geographical elements, referred to here as geographical interrelationships thinking, is a crucial part of geographical synthetic thinking. Causal diagrams are commonly employed in geography to cultivate students' geographical synthetic thinking. To test the hypothesis that teaching with geographical causal diagrams can enhance students' geographical interrelationships thinking, the author designed a teaching experiment. A total of 37 high school students participated in the one-month four-lesson teaching experiment. The results showed a significant improvement in students' geographical interrelationships thinking. There was a significant positive correlation between students' proficiency in drawing causal diagrams and their geographical interrelationships thinking. [ABSTRACT FROM AUTHOR]

The level of knowledge on the non-thermal contribution of ultrasonic wave’s energy to perform physico-chemical phenomena is one of the bottlenecks for the commercialization purposes. Under constant nominal power of transducer (Pn), the input electrical power (Pin) is less and sensitive to the medium’s physical properties. This study attempts to assess the conversion of acoustic to thermal power experimentally and numerically using COMSOL Multiphysis@ for a 24 kHz horn-type sonicator through a medium without any sono-chemical effect. Single- and homogeneous two-phase Newtonian mixtures of sunflower oil and water (o/w) with a relatively wide range of density (914–998 kg/m3) and viscosity (0.5–63.5 mPa.s) were irradiated in a lab-scale vessel (1 L) under batch and continuous flow configuration. The direct influence of Pn (80–400 W) and o/w ratio (0–1) on temperature rise and subsequent thermo-physical properties of liquid and the indirect influence on Pin and thermal energy conversion (TEC) were investigated employing calorimetric method. A new engineering concept including a power factor correlation was proposed and validated for prediction of Pin as a function of liquid space velocity (ϑ), temperature, Prandtl (Pr) and Ohnesorge (Oh) dimensionless groups. The results showed that under constant temperature and Pn, increasing Pr and Oh increased Pin with a similar trend for both modes of operation. An increase in temperature directly led to a decrease in Pin with a power factor closed to “-1”. The Pin in continuous flow was higher compared to batch configuration at similar temperature, liquid properties, and Pn. This effect was more significant with increasing ϑ. An increase in ϑ at constant Pn led to a decrease in the inlet/outlet temperature difference in continuous flow and an increase in Pin. Increasing Pn resulted in higher TEC for both configurations; however, TEC was relatively lower in continuous flow than batch configuration indicating more efficient sonication in continuous flow.

Abstract Background Discovering causality in environmental systems is challenging because frequently controlled experiments or numerical simulations are difficult. Algorithms to learn directed acyclic graphs from system data are powerful, but they often result in too many possible causal structures that cannot be properly evaluated. Results An approach to this problem proposed here is to initially restrict the system to a target variable with its two major drivers. Subsequently, testable causal structures are obtained from rules to infer directed acyclic graphs and expert knowledge. The proposed approach, which is essentially based on correlation and regression, was applied to understand drivers of a toxic algal bloom in the Odra River in summer 2022. Through this application, useful insight on the interplay between river flow and salt inputs that likely caused the algal bloom was obtained. Conclusions The Odra River example demonstrated that carefully applied correlation and regression techniques together with expert knowledge can help to discover reliable casual structures in environmental systems.

Background: Discovering causality in environmental systems is challenging because frequently controlled experiments or numerical simulations are difficult. Algorithms to learn directed acyclic graphs from system data are powerful, but they often result in too many possible causal structures that cannot be properly evaluated. Results: An approach to this problem proposed here is to initially restrict the system to a target variable with its two major drivers. Subsequently, testable causal structures are obtained from rules to infer directed acyclic graphs and expert knowledge. The proposed approach, which is essentially based on correlation and regression, was applied to understand drivers of a toxic algal bloom in the Odra River in summer 2022. Through this application, useful insight on the interplay between river flow and salt inputs that likely caused the algal bloom was obtained. Conclusions: The Odra River example demonstrated that carefully applied correlation and regression techniques together with expert knowledge can help to discover reliable casual structures in environmental systems. [ABSTRACT FROM AUTHOR]

To ensure the successful implementation of Nature-Based Solutions (NBS), it is essential to assess the perception of key stakeholders. To visualize the perception of NBS key stakeholders and to identify potential opportunities and eventual conflicting interests, five groups of stakeholders were identified: (i) academics, (ii) governments, (iii) NGOs, (iv) water companies, and (v) local communities. Semi-structured interviews with the representatives of these five groups were conducted across Iceland. The interviews were transcribed using Vivo coding, an open-coded method. 170 variables were found within codes and classified into Independent, Dependent, and Intervening Groups of variables (InDGs, DGs, and IGs, respectively). These variables were mapped using the Vensim model to visualize the causality of each variable with one another. The Vensim model identified 85 InDGs, 43 DGs, and 42 IGs. The InDGs and DGs were classified into themes to visualize the effect of variables on 13 topical groups in Causal Diagrams (CDs) focusing on NBS and Sludge Treatment Reed Bed (STRB) technology. A causal loop between biodiversity, sustainability, and seawater quality was identified in the aggregated CD. This study sheds light on the reforms needed in the Icelandic system to facilitate resource recovery from sewage sludge in Iceland using NBS and STRB. Hence, our findings provide policy and decision-makers with intra-community information to identify factors influencing STRB application.

The interventions aimed at improving the levels of physical activity (PA) in children and adolescents diagnosed with heart disease did not produce the expected outcomes. Safe participation in sport activities proposed based on actual recommendations could be a solution to promote PA in this population. The aims of this study were to discover a causal diagram of sport participation in children and youth with heart disease and establish the factors that affect and are affected thereof through the use of questionnaires. Furthermore, the study aims to qualitatively assess the reliability of the constructed diagram in comparison with existing medical knowledge. The Greedy Fast Causal Inference method was employed to conduct a data-driven search of the directed acyclic graph that represents the causal relationships within the provided observational data. This causal discovery was performed using the Tetrad software. The analysis involved a cohort of 121 Caucasian patients (50 females) diagnosed with heart disease. The age range of the patients included in the study was 8–17 years. The study findings indicate that the participants engaged in sports presented significantly higher values of health-related quality of life (QoL) and motives for participating in physical and leisure activities. Age appears to be a cause of sport participation. Sport participation appears to be a cause of participation in physical education classes, which in turn appears to be a cause of higher enjoyment. Higher enjoyment appears to be a cause of other motives for participating in physical and leisure activities, as well as a higher score in terms of physical health. The causal diagram provided a graphical representation of the causal relationship between sport participation and better QoL with potential confounders for children and adolescents with heart disease that nearly coincided with the existing literature. Clinical trials should be designed to validate clinical utility of the presented causal diagram.

Abstract The Karamoja region of northeast Uganda had been characterized by high levels of acute malnutrition (AM) for decades, despite substantial aid investment in nutrition programmes. Participatory epidemiology (PE) was used to understand the seasonality of child AM from the perspective of women agro-pastoralists and understand their knowledge and prioritization of the causes of child AM. Women provided highly plausible descriptions and analysis of monthly variations in the occurrence of AM, livelihood factors related to the temporal variation in AM occurrence, the root causes of AM and relationships between these causes. Overall, AM was strongly attributed to declining livestock ownership and access to cow milk and normalized gender discrimination. Monthly calendars revealed important monthly patterns in AM, births and women’s workload that had not been previously reported. There was significant agreement (p

Deep learning is applied in medicine mostly due to its state-of-the-art performance for diagnostic imaging. Supervisory authorities also require the model to be explainable, but most explain the model after development (post hoc) instead of incorporating explanation into the design (ante hoc). This study aimed to demonstrate a human-guided deep learning with ante-hoc explainability by convolutional network from non-image data to develop, validate, and deploy a prognostic prediction model for PROM and an estimator of time of delivery using a nationwide health insurance database. To guide modeling, we constructed and verified association diagrams respectively from literatures and electronic health records. Non-image data were transformed into meaningful images utilizing predictor-to-predictor similarities, harnessing the power of convolutional neural network mostly used for diagnostic imaging. The network architecture was also inferred from the similarities. This resulted the best model for prelabor rupture of membranes (n = 883 , 376) with the area under curves 0.73 (95% CI 0.72 to 0.75) and 0.70 (95% CI 0.69 to 0.71) respectively by internal and external validations, and outperformed previous models found by systematic review. It was explainable by knowledge-based diagrams and model representation. This allows prognostication with actionable insights for preventive medicine. • Human learning algorithm constructed causal diagram from literatures. • Deep learning with ante-hoc explainability was the most robust prediction model. • Causal diagram and deep learning representation gained insights of the disease. [ABSTRACT FROM AUTHOR]

The Karamoja region of northeast Uganda had been characterized by high levels of acute malnutrition (AM) for decades, despite substantial aid investment in nutrition programmes. Participatory epidemiology (PE) was used to understand the seasonality of child AM from the perspective of women agro-pastoralists and understand their knowledge and prioritization of the causes of child AM. Women provided highly plausible descriptions and analysis of monthly variations in the occurrence of AM, livelihood factors related to the temporal variation in AM occurrence, the root causes of AM and relationships between these causes. Overall, AM was strongly attributed to declining livestock ownership and access to cow milk and normalized gender discrimination. Monthly calendars revealed important monthly patterns in AM, births and women's workload that had not been previously reported. There was significant agreement (p < 0.01) between independent women's groups (n = 16) for the monthly calendars and causal diagrams, indicating strong reproducibility of the methods. Triangulation indicated good validity of the monthly calendar method. The PE approach demonstrated that agro-pastoralist women with limited formal education could describe and analyse the seasonality of AM and related factors and identify and prioritize the causes of AM. Indigenous knowledge should be valued and respected, and nutrition programmes should shift the emphasis towards far more participatory and community-based approaches. The timing of conventional nutrition surveys in agro-pastoral settings should be based on an understanding of the seasonality of livelihoods. [ABSTRACT FROM AUTHOR]

Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research focused on estimating causal effects, we propose to unify the various existing definitions of selection bias in the literature by considering any bias away from the true causal effect in the referent population (the population before the selection process), due to selecting the sample from the referent population, as selection bias. Given this unified definition, selection bias can be further categorized into two broad types: type 1 selection bias owing to restricting to one or more level(s) of a collider (or a descendant of a collider) and type 2 selection bias owing to restricting to one or more level(s) of an effect measure modifier. To aid in explaining these two types-which can co-occur-we start by reviewing the concepts of the target population, the study sample, and the analytic sample. Then, we illustrate both types of selection bias using causal diagrams. In addition, we explore the differences between these two types of selection bias, and describe methods to minimize selection bias. Finally, we use an example of "M-bias" to demonstrate the advantage of classifying selection bias into these two types. [ABSTRACT FROM AUTHOR]

With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV—non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank. [ABSTRACT FROM AUTHOR]

We studied the effect of adjunct displays on recall in anexpository text (based on McCrudden, Schraw, Lehman, &Poliquin, 2007) in order to find out which means of displayaided pupils in the last years of secondary school to recallinformation. We included four conditions in the experiment:text only, text and causal diagram, text and images and causaldiagram only. Participants were checked for their recall ofmain ideas and causal sequences. Recall for main ideas didnot vary significantly across conditions. Contrary toMcCrudden et al. (2007), our results for the causal sequencesrevealed that participants who studied a causal diagram onlycould recall more steps from causal sequences thanparticipants in any of the other conditions. We will interpretthe findings in the light of the literature on redundancyeffects, dual coding theory, and the causal explicationhypothesis.

Causal inference is often the goal of psychological research. However, most researchers refrain from drawing causal conclusions based on non-experimental evidence. Despite the challenges associated with producing causal evidence from non-experimental data, it is crucial to address causal questions directly rather than avoiding them. Here we provide a clear, non-technical overview of the fundamental concepts (including the counterfactual framework and related assumptions) and tools that permit causal inference in non-experimental data, intended as a starting point for readers unfamiliar with the literature. Certain tools, such as the target trial framework and causal diagrams, have been developed to assist with the identification and reduction of potential biases in study design and analysis and the interpretation of findings. We apply these concepts and tools to a motivating example from the body image field. We assert that more precise and detailed elucidation of the barriers to causal inference within one's study is arguably a key first step in the enhancement of non-experimental research and future intervention development and evaluation. • Causal inference from non-experimental data relies on assumptions. • Target trial emulation and causal diagrams guide analysis planning and interpretation. • Pursuing answers to causal questions remains valuable even in the presence of barriers. • Our tutorial provides a clear overview of and concretise concepts with a worked example. [ABSTRACT FROM AUTHOR]

[Display omitted] • Energy conversion in a horn type sonicator was investigated under batch and continuous flow. • Acoustic power was varied depending on the liquid properties at a fixed nominal power of transducer. • A power relation was proposed for acoustic power prediction as a function of dimensionless groups. • Temperature, Pr and Oh numbers are the mains factors affecting acoustic to nominal power ratio. • Lower thermal energy conversion was obtained in continuous flow compared to batch configuration. The level of knowledge on the non-thermal contribution of ultrasonic wave's energy to perform physico-chemical phenomena is one of the bottlenecks for the commercialization purposes. Under constant nominal power of transducer (P n), the input electrical power (P in) is less and sensitive to the medium's physical properties. This study attempts to assess the conversion of acoustic to thermal power experimentally and numerically using COMSOL Multiphysis@ for a 24 kHz horn-type sonicator through a medium without any sono-chemical effect. Single- and homogeneous two-phase Newtonian mixtures of sunflower oil and water (o/w) with a relatively wide range of density (914–998 k g / m 3) and viscosity (0.5–63.5 m P a. s) were irradiated in a lab-scale vessel (1 L) under batch and continuous flow configuration. The direct influence of P n (80–400 W) and o/w ratio (0–1) on temperature rise and subsequent thermo-physical properties of liquid and the indirect influence on P in and thermal energy conversion (TEC) were investigated employing calorimetric method. A new engineering concept including a power factor correlation was proposed and validated for prediction of P in as a function of liquid space velocity (ϑ), temperature, Prandtl (Pr) and Ohnesorge (Oh) dimensionless groups. The results showed that under constant temperature and P n , increasing Pr and Oh increased P in with a similar trend for both modes of operation. An increase in temperature directly led to a decrease in P in with a power factor closed to " - 1 ". The P in in continuous flow was higher compared to batch configuration at similar temperature, liquid properties, and P n. This effect was more significant with increasing ϑ. An increase in ϑ at constant P n led to a decrease in the inlet/outlet temperature difference in continuous flow and an increase in P in. Increasing P n resulted in higher TEC for both configurations; however, TEC was relatively lower in continuous flow than batch configuration indicating more efficient sonication in continuous flow. [ABSTRACT FROM AUTHOR]

Epigenome-wide mediation analysis aims to identify high-dimensional DNA methylation at cytosine–phosphate–guanine (CpG) sites that mediate the causal effect of linking smoking with Crohn's disease (CD) outcome. Studies have shown that smoking has significant detrimental effects on the course of CD. So we assessed whether DNA methylation mediates the association between smoking and CD. Among 103 CD cases and 174 controls, we estimated whether the effects of smoking on CD are mediated through DNA methylation CpG sites, which we referred to as causal mediation effect. Based on the causal diagram, we first implemented sure independence screening (SIS) to reduce the pool of potential mediator CpGs from a very large to a moderate number; then, we implemented variable selection with de-sparsifying the LASSO regression. Finally, we carried out a comprehensive mediation analysis and conducted sensitivity analysis, which was adjusted for potential confounders of age, sex, and blood cell type proportions to estimate the mediation effects. Smoking was significantly associated with CD under odds ratio (OR) of 2.319 (95% CI: 1.603, 3.485, p < 0.001) after adjustment for confounders. Ninety-nine mediator CpGs were selected from SIS, and then, seven candidate CpGs were obtained by de-sparsifying the LASSO regression. Four of these CpGs showed statistical significance, and the average causal mediation effects (ACME) were attenuated from 0.066 to 0.126. Notably, three significant mediator CpGs had absolute sensitivity parameters of 0.40, indicating that these mediation effects were robust even when the assumptions were slightly violated. Genes (BCL3 and FKBP5) harboring these four CpGs were related to CD. These findings suggest that changes in methylation are involved in the mechanism by which smoking increases risk of CD. [ABSTRACT FROM AUTHOR]

Epigenome-wide mediation analysis aims to identify high-dimensional DNA methylation at cytosine–phosphate–guanine (CpG) sites that mediate the causal effect of linking smoking with Crohn’s disease (CD) outcome. Studies have shown that smoking has significant detrimental effects on the course of CD. So we assessed whether DNA methylation mediates the association between smoking and CD. Among 103 CD cases and 174 controls, we estimated whether the effects of smoking on CD are mediated through DNA methylation CpG sites, which we referred to as causal mediation effect. Based on the causal diagram, we first implemented sure independence screening (SIS) to reduce the pool of potential mediator CpGs from a very large to a moderate number; then, we implemented variable selection with de-sparsifying the LASSO regression. Finally, we carried out a comprehensive mediation analysis and conducted sensitivity analysis, which was adjusted for potential confounders of age, sex, and blood cell type proportions to estimate the mediation effects. Smoking was significantly associated with CD under odds ratio (OR) of 2.319 (95% CI: 1.603, 3.485, p

With the advent of the big data era, the need to combine multiple individual data sets to draw causal effects arises naturally in many medical and biological applications. Especially each data set cannot measure enough confounders to infer the causal effect of an exposure on an outcome. In this article, we extend the method proposed by a previous study to causal data fusion of more than two data sets without external validation and to a more general (continuous or discrete) exposure and outcome. Theoretically, we obtain the condition for identifiability of exposure effects using multiple individual data sources for the continuous or discrete exposure and outcome. The simulation results show that our proposed causal data fusion method has unbiased causal effect estimate and higher precision than traditional regression, meta‐analysis and statistical matching methods. We further apply our method to study the causal effect of BMI on glucose level in individuals with diabetes by combining two data sets. Our method is essential for causal data fusion and provides important insights into the ongoing discourse on the empirical analysis of merging multiple individual data sources. [ABSTRACT FROM AUTHOR]

In the absence of an established gold standard, an understanding of the testing cycle from individual exposure to test outcome report is required to guide the correct interpretation of severe acute respiratory syndrome-coronavirus-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes. Bayesian network models have been used within healthcare to bring clarity to complex problems. We use this modelling approach to construct a comprehensive framework for understanding the real-world predictive value of individual RT-PCR results. We elicited knowledge from domain experts to describe the test process through a facilitated group workshop. A preliminary model was derived based on the elicited knowledge, then subsequently refined, parameterised and validated with a second workshop and one-on-one discussions. Causal relationships elicited describe the interactions of pre-testing, specimen collection and laboratory procedures and RT-PCR platform factors, and their impact on the presence and quantity of virus and thus the test result and its interpretation. By setting the input variables as 'evidence' for a given subject and preliminary parameterisation, four scenarios were simulated to demonstrate potential uses of the model. The core value of this model is a deep understanding of the total testing cycle, bridging the gap between a person's true infection status and their test outcome. This model can be adapted to different settings, testing modalities and pathogens, adding much needed nuance to the interpretations of results. [ABSTRACT FROM AUTHOR]

The decision-making trial and evaluation laboratory (DEMATEL) method has been applied to solve numerous multi-criteria decision-making (MCDM) problems where crisp numbers are utilized in defining linguistic evaluation. Previous literature suggests that the intuitionistic fuzzy DEMATEL (IF-DEMATEL) can offer a new decision-making method in solving MCDM problems where intuitionistic fuzzy sets (IFSs) are utilized in defining linguistic evaluation. This paper aims to develop a cause–effect diagram of subcontractor selection using a modified IF-DEMATEL method. In this paper, three modifications are made to the IF-DEMATEL method. Two memberships of IFSs, relative weights of experts, and a transformation equation are the elements introduced to the IF-DEMATEL. The linguistic variables that defined in IFSs are meant to capture wide arrays of uncertain and fuzzy information in solving MCDM problems. Furthermore, the modified IF-DEMATEL is applied it to a subcontractors' selection problem where groups of cause and effect criteria are segregated. A group of experts' opinions were sought to provide linguistic evaluations regarding the degree of influence between criteria in subcontractors' selection. The results show that four criteria are identified as cause criteria while six other criteria are identified as effect criteria. The results also suggest that the criteria “experience” is the main cause that influence the selection of subcontractors. The identification of cause and effect criteria would be a great significance for practical implementation of subcontractors' selection.

Research Spotlights (practice and policy-oriented abstract) More researchers in academia and industry are now conducting randomized experiments, collecting information beyond just the subjects' treatment assignments and outcomes. Designing and conducting experiments is costly to researchers and subjects; therefore, it is important to derive greater value from such data. Our article describes how researchers can make use of such extra information by using causal diagrams, by applying a few simple rules based on Judea Pearl's causal diagramming framework. This allows researchers to find more nuanced insights about the underlying theoretical mechanisms and to generalize the inferences. We illustrate this process by creating causal diagrams for a large randomized field experiment on online dating. We describe the insights that this process reveals. Researchers using randomized controlled trials (RCTs) often subgroup or condition on auxiliary variables that are not the randomized treatment variable. There are many good reasons to condition on auxiliary variables—also referred to as control variables or covariates—in randomized experiments. In particular, designing and conducting RCTs is costly to researchers and subjects, and therefore it is important to derive greater value from RCT data; measuring not just the average treatment effect (ATE), but also finding more nuanced insights about the underlying theoretical mechanisms and generalizing the inferences. Unfortunately, there are many confusing and even contradictory guidelines on the use of subgroups or auxiliary variables in RCTs. We show how researchers can leverage covariates without biasing their causal inferences, by applying a few simple rules based on Judea Pearl's causal diagramming framework. We demonstrate how to create a causal schema, through careful and deliberate operationalization of auxiliary covariates, in order to analyze the intermediate effects along a causal chain from the treatment to outcome; and we discuss some other ways to leverage covariates for theory development and generalization of findings from RCTs. We present a criterion for distinguishing pretreatment and posttreatment variables that is based on directed acyclic graphs (DAGs). We provide a succinct set of guidelines to help readers begin to employ some essential techniques of DAG-based causal analysis. Finally, we provide a series of short tutorials (with accompanying simulated data and R scripts) to help readers explore the connections between RCT and observational contexts in causal diagramming. This commentary aims to raise awareness of the DAG methodology, explain its usefulness to experimental research, and encourage adoption in the IS community for studies using RCTs as well as observational data. [ABSTRACT FROM AUTHOR]

Foetal alcohol spectrum disorders (FASDs) are a leading cause of developmental disability. Prenatal alcohol use is the sole necessary cause of FASD, but it is not always sufficient. Multiple factors influence a child's susceptibility to FASD following prenatal alcohol exposure. Much of the FASD risk factor literature has been limited to discussions of association, rather than causation. While knowledge of predictor variables is important for identifying who is most at risk of FASD and for targeting interventions, causal knowledge is important for identifying effective mechanisms for prevention and intervention programmes. We conducted a systematic search and narrative synthesis of the evidence and used this to create a causal diagram (directed acyclic graph; DAG) to describe the causal pathways to FASD. Our results show that the aetiology of FASD is multifaceted and complex. FASD risk is determined by a range of lifestyle, sociodemographic, maternal, social, gestational, and genetic factors. The causal diagram that we present in this review provides a comprehensive summary of causal risk factors for FASD and can be used as a tool to inform data collection and statistical modelling strategies to minimise bias in future studies of FASD. [ABSTRACT FROM AUTHOR]

The interventions aimed at improving the levels of physical activity (PA) in children and adolescents diagnosed with heart disease did not produce the expected outcomes. Safe participation in sport activities proposed based on actual recommendations could be a solution to promote PA in this population. The aims of this study were to discover a causal diagram of sport participation in children and youth with heart disease and establish the factors that affect and are affected thereof through the use of questionnaires. Furthermore, the study aims to qualitatively assess the reliability of the constructed diagram in comparison with existing medical knowledge. The Greedy Fast Causal Inference method was employed to conduct a data-driven search of the directed acyclic graph that represents the causal relationships within the provided observational data. This causal discovery was performed using the Tetrad software. The analysis involved a cohort of 121 Caucasian patients (50 females) diagnosed with heart disease. The age range of the patients included in the study was 8-17 years. The study findings indicate that the participants engaged in sports presented significantly higher values of health-related quality of life (QoL) and motives for participating in physical and leisure activities. Age appears to be a cause of sport participation. Sport participation appears to be a cause of participation in physical education classes, which in turn appears to be a cause of higher enjoyment. Higher enjoyment appears to be a cause of other motives for participating in physical and leisure activities, as well as a higher score in terms of physical health. The causal diagram provided a graphical representation of the causal relationship between sport participation and better QoL with potential confounders for children and adolescents with heart disease that nearly coincided with the existing literature. Clinical trials should be designed to validate clinical utility of the presented causal diagram., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Gąsior, Młyńczak, Williams, Popłonyk, Kowalska, Giezek and Werner.)

When causal effects are to be estimated from observational data, we have to adjust for confounding. A central aim of covariate selection for causal inference is therefore to determine a set that is sufficient for confounding adjustment, but other aims such as efficiency or robustness can be important as well. In this paper, we review six general approaches to covariate selection that differ in the targeted type of adjustment set. We discuss and illustrate their advantages and disadvantages using causal diagrams. Moreover, the approaches and different ways of implementing them are compared empirically in an extensive simulation study. We conclude that there are considerable differences between the approaches but none of them is uniformly best, with performance depending on the chosen adjustment method as well as the true confounding structure. Any prior structural knowledge on the causal relations is helpful to choose the most appropriate method. [ABSTRACT FROM AUTHOR]

Major factor in the electricity production, the number of hydropower plants is still steadily growing worldwide. Besides, such facilities entail a whole range of effects. A sound and in-depth assessment of all the impacts arising from each new project is required. While sustainability assessment tools provide relevant instruments in this aim, as they enable the evaluation of aspects from various kinds, interrelations between the latter are frequently overlooked. Based on the available literature, we use the concept of the causal diagram to structure and represent hydropower impacts in a generic form through causal networks. We develop and present a series of ten causal diagrams, each focusing on one specific topic. Strongly interrelated, the diagrams highlight the close ties existing between the multiple aspects of hydropower and enable a clear understanding of such links. A plain grasp of the impact pathways is essential to gain a holistic and comprehensive perspective of the consequences ensuing from the implementation of hydropower facilities. This step is also crucial in identifying relevant and concrete measures to mitigate undesirable impacts. All in all, such causal diagrams are meant to be used as guidance and supportive tools by decision makers and concerned stakeholders. • Impact pathways from hydropower implementation form a complex and extensive network. • Holistic approach is crucial to fully grasp the impacts of hydropower facilities. • Causal approach enables to seize the extent of fields altered by hydropower impacts. • Systemic perspective is extremely relevant for hydropower sustainability assessment. • Causal diagram can be a relevant tool to grasp stakeholders' multiple perspectives. [ABSTRACT FROM AUTHOR]

The fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) has been used to solve various multi-criteria group decision-making problems where triangular fuzzy numbers are utilized in defining decision makers' linguistic judgements. Most of the fuzzy DEMATEL modifications are built from linguistic variables based on fuzzy sets. Recent literature suggests that Pythagorean fuzzy sets (PFS) can offer a better alternative particularly when fuzzy sets have some extent of limitations in handling vagueness and uncertainty. This paper proposes a modification fuzzy DEMATEL characterized by PFS for linguistic variables. Differently from the typical fuzzy DEMATEL which directly utilizes triangular fuzzy numbers with a single membership, this modification introduces membership and non-membership of PFS to enhance judgements in the group decision-making environment. The proposed method has a number of attractive features. It includes linguistic variables, expert's weights, and score function, in which all of these features are expressed by PFS. The proposed modification is applied to a case of solid waste management (SWM) where ten criteria are considered in assessment. Six experts in SWM were invited to provide linguistic judgments with respect to the criteria, and the eleven-step computational procedure of the proposed method was implemented without losing the general structure of the DEMATEL method. The results unveiled that four criteria are identified as 'cause group' and six criteria are identified as 'effect group' in SWM. The grouping of criteria would help policy makers in identifying the criteria that could enhance the efficiency of SWM. [ABSTRACT FROM AUTHOR]

Abstract Industrial sensors efficiency is commonly determined by the ability to provide accurate indications during a mission. In addition, several techniques and methods are incorporated to increase sensors measurement accuracy. These examinations are classified into experimental and theoretical classes. Experimental examinations focus on determining the flow coefficient variation, which results from the installation effect and flow characteristics. Theoretical proposals deal principally with formulation of the instrument reliability function. These formulations integrate different parameters which affect the sensor measurement accuracy during a given mission. This paper discusses the failure risk and effect analysis for the Pitot sensors system measurement accuracy during a given mission. In addition, a set of factors is used to determine the causal diagram and to model the corresponding hazard function. These factors are subject to statistical analysis in the second part of this paper. The current study is also driven by a practical need to well manage aeronautical problems linked to Pitot sensors system malfunctioning during a flight. [ABSTRACT FROM AUTHOR]

This study makes a systemic review to cluster and create a competitiveness relationship considering a systems dynamics approach. A dynamic hypothesis was constructed to validate what factors increase a cluster’s level of competitiveness, through causal analysis. Then, the causal diagram that validates the dynamic H0 hypothesis was constructed in Vensim PLE systems®. Literature review shows the evolution of the cluster system according to the current needs of the market, and emphasizes the need for new approaches and models that capture the complexity and dynamics of this system, allowing the understanding of its structure and the evaluation of the contribution of factors and capabilities to cluster competitiveness. It highlights the usefulness of systems dynamics as a simulation methodology for dynamic and complex systems, and establishes itself as a growing line of research applied to various systems of study. Dynamic hypothesis H0 was validated using the causal diagram, reaching the conclusion that innovation, productive management, financial management, organizational management, commercial management, and cluster management factors positively increase the cluster competitiveness level. From structure analysis, the behavior is associated to the archetype “Path Dependence”, usual in growing industrial markets.

This paper presents a description of the sustainability problem faced by biofuel production systems, specifically the production of bioethanol in Colombia, from a systemic precept developed by causal diagrams of the problem. To understand the sustainability of bioenergy and biofuels, there is a set of 24 sustainability indicators that may be useful for economies that are entering bioenergy or have a consolidated production such as the Colombian case. In this way, the production stages are described where these indicators apply to the Colombian context of sugarcane production. Finally, to illustrate how the indicators are interconnected in the production process, a causal diagram was obtained that emerges from the systemic conceptualization of the problem, which allowed the qualitative capture of the main attributes of the system with the respective causal relationships and typical feedback structures of the system thinking. [ABSTRACT FROM AUTHOR]