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The risk factors in banking have been considered an undesirable carryover variable by the literature. Methodologically, we consider the risk factor using loan loss reserves as a desirable carryover input with dynamic characteristics, which provides a new framework in the dynamic network Data Envelopment Analysis (DEA) modelling. We substantiate our formulation and results using novel techniques for causal modelling to ensure that our dynamic network model admits a causal interpretation. Finally, we empirically examine the impact of risk from various economic sectors on efficiency. Our results show that the inefficiencies were volatile in Chinese banking over the period 2013–2020, and we further find that the state-owned banks experienced the highest levels of inefficiency and volatility. The findings report that credit risk derived from the agricultural sector and the Water Conservancy, Environment and Public Facilities management sector decreases bank efficiency, while credit risk derived from the wholesale and retail sector improves bank efficiency. The results of our innovative causal modelling show that our pioneering modelling on the role of loan loss reserves is valid. In addition, from an empirical perspective, our second-stage analysis regarding the impact of risk derived from different economic sectors on bank efficiency can be applied to other banking systems worldwide because of our successful validation from causal modelling. Our attempt to incorporate causal inference into DEA can be generalized to future studies of using DEA for performance evaluation. [ABSTRACT FROM AUTHOR]

Simpson’s paradox (SP) is a statistical phenomenon where the association between two variables reverses, disappears, or emerges, after conditioning on a third variable. It has been proposed (by, e.g., Judea Pearl) that SP should be analyzed using the framework of graphical causal models (i.e., causal DAGs) in which SP is diagnosed as a symptom of confounding bias. This paper contends that this confounding-based analysis cannot fully capture SP: there are cases of SP that cannot be explained away in terms of confounding. Previous works have argued that some cases of SP do not require causal analysis at all. Despite being a logically valid counterexample, we argue that this type of cases poses only a limited challenge to Pearl’s analysis of SP. In our view, a more powerful challenge to Pearl comes from cases of SP that do require causal analysis but can arise without confounding. We demonstrate with examples that accidental associations due to genetic drift, the use of inappropriate aggregate variables as causes, and interactions between units (i.e., inter-unit causation) can all give rise to SP of this type. The discussion is also extended to the amalgamation paradox (of which SP is a special form) which can occur due to the use of non-collapsible association measures, in the absence of confounding. [ABSTRACT FROM AUTHOR]

The expensive-tissue hypothesis (ETH) posited a brain–gut trade-off to explain how humans evolved large, costly brains. Versions of the ETH interrogating gut or other body tissues have been tested in non-human animals, but not humans. We collected brain and body composition data in 70 South Asian women and used structural equation modelling with instrumental variables, an approach that handles threats to causal inference including measurement error, unmeasured confounding and reverse causality. We tested a negative, causal effect of the latent construct ‘nutritional investment in brain tissues’ (MRI-derived brain volumes) on the construct ‘nutritional investment in lean body tissues’ (organ volume and skeletal muscle). We also predicted a negative causal effect of the brain latent on fat mass. We found negative causal estimates for both brain and lean tissue (−0.41, 95% CI, −1.13, 0.23) and brain and fat (−0.56, 95% CI, −2.46, 2.28). These results, although inconclusive, are consistent with theory and prior evidence of the brain trading off with lean and fat tissues, and they are an important step in assessing empirical evidence for the ETH in humans. Analyses using larger datasets, genetic data and causal modelling are required to build on these findings and expand the evidence base.

In this article we set out to understand the significance of the process matrix formalism and the quantum causal modelling programme for ongoing disputes about the role of causation in fundamental physics. We argue that the process matrix programme has correctly identified a notion of ‘causal order’ which plays an important role in fundamental physics, but this notion is weaker than the common-sense conception of causation because it does not involve asymmetry. We argue that causal order plays an important role in grounding more familiar causal phenomena. Then we apply these conclusions to the causal modelling programme within quantum foundations, arguing that since no-signalling quantum correlations cannot exhibit causal order, they should not be analysed using classical causal models. This resolves an open question about how to interpret fine-tuning in classical causal models of no-signalling correlations. Finally we observe that a quantum generalization of causal modelling can play a similar functional role to standard causal reasoning, but we emphasize that this functional characterisation does not entail that quantum causal models offer novel explanations of quantum processes. [ABSTRACT FROM AUTHOR]

Work-related stress has emerged as a pervasive global issue that needs to be investigated by specialists around the world. Work stress is a condition of pressure brought on by one's line of work and occurs when demands of the job are too great for an employee's abilities or resources. The study was undertaken to examine the direct and indirect causal effect of some demographic variables on workstimulated stress among early childhood educators in South Africa. Hence, the researchers developed and validated a model involving causal linkages between early childhood educators' demographic variables such as age, gender, race, marital status, income and educational qualification, and workstimulated stress. The study adopted an ex-post-facto research design. The sample comprised one hundred and twenty (120) early childhood educators across twenty (20) Early Childhood Education (ECE) centres. A stratified random sampling technique was used to select the early childhood educators for the study. One validated instrument on work-stimulated stress developed by the researchers on a four-point rating scale was used to collect the data for the study, while path analysis and multiple regression analysis were employed for data analysis. The findings of this study documented the more parsimonious model, which is effective in predicting the influence of demographic variables on work-stimulated stress among early childhood educators. The results further indicated that three (Age, Gender, and Marital Status) out of the six predictor variables caused early childhood educators' work-stimulated stress more than the other variables. The implications of these findings for education policymakers, administrators, and teachers are discussed. [ABSTRACT FROM AUTHOR]

Evolutionary theory has made large impacts on our understanding and management of the world, in part because it has been able to incorporate new data and new insights successfully. Nonetheless, there is currently a tension between certain biological phenomena and mainstream evolutionary theory. For example, how does the inheritance of molecular epigenetic changes fit into mainstream evolutionary theory? Is niche construction an evolutionary process? Is local adaptation via habitat choice also adaptive evolution? These examples suggest there is scope (and perhaps even a need) to broaden our views on evolution. We identify three aspects whose incorporation into a single framework would enable a more generalised approach to the understanding and study of adaptive evolution: (i) a broadened view of extended phenotypes; (ii) that traits can respond to each other; and (iii) that inheritance can be non‐genetic. We use causal modelling to integrate these three aspects with established views on the variables and mechanisms that drive and allow for adaptive evolution. Our causal model identifies natural selection and non‐genetic inheritance of adaptive parental responses as two complementary yet distinct and independent drivers of adaptive evolution. Both drivers are compatible with the Price equation; specifically, non‐genetic inheritance of parental responses is captured by an often‐neglected component of the Price equation. Our causal model is general and simplified, but can be adjusted flexibly in terms of variables and causal connections, depending on the research question and/or biological system. By revisiting the three examples given above, we show how to use it as a heuristic tool to clarify conceptual issues and to help design empirical research. In contrast to a gene‐centric view defining evolution only in terms of genetic change, our generalised approach allows us to see evolution as a change in the whole causal structure, consisting not just of genetic but also of phenotypic and environmental variables. [ABSTRACT FROM AUTHOR]

Experience shows that Agile project management tools such as Atlassian Jira capture the state of EAS projects by relying solely on expert judgement that is not supported by any knowledge model. Therefore, the assessment of project content against strategic objectives and business domain features are not supported by any tool. This is one of the reasons why Agile project management still does not provide sufficient EAS project delivery results. In order to address this problem, the Enterprise Application Software (EAS) development using Agile project management is summarized in a conceptual model. The model highlights the knowledge used and indicates its nature (empirical or causal digitized). The modified Agile management process we have developed and described in previous works is based on causal knowledge models that supports EAS development and Agile management processes. The purpose of this article is to specify knowledge repository to ensure the Agile management solutions of an EAS project are aligned with strategic goals and business domain causality. It is worth noticing that strategic goals have been identified and specified as capabilities using some enterprise architecture framework (NAF, MODAF, ArchiMate, etc.). The novelty of the proposed method is incorporating the business domain causal knowledge modelling approach into the Agile project management process. The causal knowledge unit is considered as a Management Transaction (MT), which includes closed loop dependence of its components. The modified Agile activity hierarchy (theme, initiative, epic, user story) defines the required content of their mutual interactions. An important new results obtained are the conceptual model of causal knowledge base (KB) and specification of enhanced Agile management tool components: project management database and project state assesment knowledge base. Causal KB includes specification of causal knowledge unit (MT metamodel) and specifications of traditional and causal Agile hierarchy meta-models. These conceptual models define the causal knowledge components necessary to evaluate the state of Agile activities in the EAS development project using intelligent Agile project management tool. [ABSTRACT FROM AUTHOR]

Discrimination law is a possible application of the methods of causal modelling. With it, it brings the possibility of direct statistical evidence on counterfactual questions, something that traditional techniques like multiple regression lack. The kinds of evidence that causal modelling can provide, in large part due to its attention to counterfactuals, is very close to the key question that we ask of jurors in discrimination cases. With this new kind of evidence comes new opportunities. We can better proportion punitive damages to the severity of the discrimination that manifests in a hiring process. We can avoid making certain kinds of assumptions regarding the relationship between protected classes and hiring qualifications that other statistical methods demand from statisticians. We can also distribute restitution to individual claimants in a way that is proportionate to how their application was treated in the hiring process. Here we explore where and how causal modelling can be useful in discrimination law and policy. What elements of law provide friction with this mode of gathering statistical evidence, what new possibilities does it reveal, and how does this integrate with prior judgments regarding statistical evidence? [ABSTRACT FROM AUTHOR]

In recent decades, economists have developed methods for measuring the country-wide level of inequality of opportunity. The most popular method, called the ex-ante method, uses data on the distribution of outcomes stratified by groups of individuals with the same circumstances, in order to estimate the part of outcome inequality that is due to these circumstances. I argue that these methods are potentially biased, both upwards and downwards, and that the unknown size of this bias could be large. To argue that the methods are biased, I show that they ought to measure causal or counterfactual quantities, while the methods are only capable of identifying correlational information. To argue that the bias is potentially large, I illustrate how the causal complexity of the real world leads to numerous non-causal correlations between circumstances and outcomes and respond to objections claiming that such correlations are nonetheless indicators of unfair disadvantage, that is, inequality of opportunity. [ABSTRACT FROM AUTHOR]

A predictive understanding of adaptation to changing environments hinges on a mechanistic understanding of the extent and causes of variation in natural selection. Estimating variation in selection is difficult due to the complex relationships between phenotypic traits and fitness, and the uncertainty associated with individual selection estimates. Plant-pollinator interactions provide ideal systems for understanding variation in selection and its predictability, because both the selective agents (pollinators) and the process linking phenotypes to fitness (pollination) are generally known. Through examples from the pollination literature, I discuss how explicit consideration of the functional mechanisms underlying trait-performance relationships can clarify the relationship between traits and fitness, and how variation in the ecological context that generates selection can help disentangle biologically important variation in selection from sampling variation. I then evaluate the predictability of variation in pollinator-mediated selection through a survey, reanalysis, and synthesis of results from the literature. The synthesis demonstrates that pollinator-mediated selection often varies substantially among trait functional groups, as well as in time and space. Covariance between patterns of selection and ecological variables provides additional support for the biological importance of observed selection, but the detection of such covariance depends on careful choice of relevant predictor variables as well as consideration of quantitative measurements and their meaning, an aspect often neglected in selection studies.

Abstract Background The explosion caused by the fusion of quantitative genetics and molecular genetics will transform behavioural genetic research in child and adolescent psychology and psychiatry. Methods Although the fallout has not yet settled, the goal of this paper is to predict the next 10 years of research in what could be called behavioural genomics. Results I focus on three research directions: the genetic architecture of psychopathology, causal modelling of gene‐environment interplay, and the use of DNA as an early warning system. Conclusion Eventually, whole‐genome sequencing will be available for all newborns, which means that behavioural genomics could potentially be applied ubiquitously in research and clinical practice.

Planning for the future plays a pivotal role in a competitive business world. Scenario analysis is a popular tool for exploring plausible futures and planning. However, the practice of scenario planning is often qualitative, unstructured, and time-consuming. We propose a structured technology development framework by categorising the qualitative variables impacting technology development and identifying their causal relationships. We then use causal loops and expert opinions and the Decision Making Trial and Evaluation Laboratory (DEMATEL) method for scenario planning and futures studies. We present a case study in the communications industry to demonstrate the applicability of the proposed framework. [ABSTRACT FROM AUTHOR]

The Agile approach and tools are popular for the management of Enterprise Application Software (EAS) development. This article focuses on the issue of inconsistency between strategic business objectives and the functionality of the software developed. Agile management tools lack the functionality of EAS project activities coordination. This article aims to rethink Agile project management using the causal modelling approach. A causal model of Agile project management using a management transaction (MT) concept was developed. The notion of the space of processes was used to identify the MTs location along the axes of aggregation, generalization, and time and to formalize their interaction specifications. Taxonomy of the coordination meta-types and types was developed using the identifiers of the MTs. The modified Agile activities hierarchy was developed, and vertical and horizontal causal interactions between Agile activities were identified. This modified Agile management model helps to consistently track the integrity of EAS project content. Complexity indicators were introduced to evaluate the EAS project complexity and their average and normalized values are presented. Additional attributes in the Agile management tool Jira are proposed. Monitoring mismatch between strategic business objectives and development activities content helps to improve the success of EAS projects delivery. [ABSTRACT FROM AUTHOR]

Runway excursion (RE) is recognized as one of the main Airport Safety Issues. It can be defined as an event in which an aircraft veers-off or overruns the runway surface during either takeoff or landing. In this paper an application of Fault Tree Analysis (FTA) method on RE events is presented. FTA belongs to quantitative class of causal methods. It estimates the risk of incident or accident according to estimation of probability of occurrence of each cause of an event. It might be restricted to pure statistical analysis based on the available data or combine these data with expert judgment on the accident causes. The aim of this paper is to identify potential causal factors. In order to illustrate FTA application on RE, two models were developed: one for landing and other for take-off. Application of FTA has shown what are the most critical causal factors whose knowledge allows developing certain measures to reduce the risk of RE.

The most serious candidates for common causes that fail to screen off ('interactive common causes', ICCs) and thus violate the causal Markov condition (CMC) refer to quantum phenomena. In her seminal debate with Hausman and Woodward, Cartwright early on focussed on unfortunate non-quantum examples. Especially, Hausman and Woodward's redescriptions of quantum cases saving the CMC remain unchallenged. This paper takes up this lose end of the discussion and aims to resolve the debate in favour of Cartwright's position. It systematically considers redescriptions of ICC structures, including those by Hausman and Woodward, and explains why these are inappropriate, when quantum mechanics (in an objective collapse interpretation) is true. It first shows that all cases of purported quantum ICCs are cases of entanglement and then, using the tools of causal modelling, it provides an analysis of the quantum mechanical formalism for the case that the collapse of entangled systems is best described as a causal model with an ICC. [ABSTRACT FROM AUTHOR]

Aim: To advance our understanding of the mechanisms that mediate the relationships between global climatic and anthropogenic processes and pathogen occurrence, it is crucial to evaluate the exact pathways connecting the ecological mediators and the pathogen responses across spatial and temporal heterogeneities at various scales. We investigated the pathways connecting these two types of heterogeneities in sand stabilization that were created by contrasting forces of various human activities and long‐term droughts, and pathogen occurrence in host populations. The considered candidate ecological mediators were various components of host community structure, arthropod vector traits, and the pathogen occurrence in these vectors. Location: North‐western Negev Desert's sands in Israel. Time period: 1982–2018. Major taxa studied: Gerbillus andersoni, Gerbillus floweri, Gerbillus gerbillus, Mycoplasma, Bartonella, Synosternus cleopatrae. Methods: We combined information from satellite images, 36 years of rodent censuses, a natural experiment, and causal modelling. Results: We found evidence that the spatial heterogeneity in sand biocrusts is largely correlated with structural differences between host communities, especially at medium spatial scales. Pathogen sampling, followed by causal modelling, suggested that the cascading effect of sand stabilization on pathogen occurrence is mainly mediated through changes in host community structure and vector burdens. Importantly, we found that structural changes in the same host community can simultaneously amplify and dilute different pathogens. Main conclusions: These findings suggest that global processes can translate into local processes, where the importance of the mediation effects depend on the magnitude of environmental heterogeneity. These mediation effects can benefit some organisms while adversely affecting others. [ABSTRACT FROM AUTHOR]

The expensive-tissue hypothesis (ETH) posited a brain-gut trade-off to explain how humans evolved large, costly brains. Versions of the ETH interrogating gut or other body tissues have been tested in non-human animals, but not humans. We collected brain and body composition data in 70 South Asian women and used structural equation modelling with instrumental variables, an approach that handles threats to causal inference including measurement error, unmeasured confounding and reverse causality. We tested a negative, causal effect of the latent construct 'nutritional investment in brain tissues' (MRI-derived brain volumes) on the construct 'nutritional investment in lean body tissues' (organ volume and skeletal muscle). We also predicted a negative causal effect of the brain latent on fat mass. We found negative causal estimates for both brain and lean tissue (-0.41, 95% CI, -1.13, 0.23) and brain and fat (-0.56, 95% CI, -2.46, 2.28). These results, although inconclusive, are consistent with theory and prior evidence of the brain trading off with lean and fat tissues, and they are an important step in assessing empirical evidence for the ETH in humans. Analyses using larger datasets, genetic data and causal modelling are required to build on these findings and expand the evidence base., Competing Interests: Meghan Shirley Bezerra, Samuli Helle, Kiran Seunarine, Owen Arthurs, Simon Eaton, Jane Williams, Chris Clark, and Jonathan Wells declare none., (© The Author(s) 2024.)

The Sustainable Development Goal (SDG) target for access to safe sanitation and hygiene represents a marked improvement over the target used during the Millennium Development Goal (MDG) period. The SDG target attempts to: explicitly address hygiene; eliminate inequalities within populations; evaluate sanitation services beyond the household; account for the accessibility, safety, acceptability, and affordability of service delivery; and improve the sustainability of services (WHO/UNICEF, 2015). However, the proposed indicators for monitoring progress in sanitation and hygiene still rely primarily on infrequent household surveys and census data. This paper provides a critical review of the sanitation and hygiene target and explores the potential gaps between the expanded understanding of access, the proposed monitoring strategies, and the desired impacts. A variety of innovative methodologies and technologies are reviewed, with specific attention given to their suitability for measuring and monitoring progress towards the sanitation and hygiene target. [ABSTRACT FROM AUTHOR]

Wildfire risks and losses have increased over the last 100 years, associated with population expansion, land use and management practices, and global climate change. While there have been extensive efforts at modeling the probability and severity of wildfires, there have been fewer efforts to examine causal linkages from wildfires to impacts on ecological receptors and critical habitats. Bayesian networks are probabilistic tools for graphing and evaluating causal knowledge and uncertainties in complex systems that have seen only limited application to the quantitative assessment of ecological risks and impacts of wildfires. Here, we explore opportunities for using Bayesian networks for assessing wildfire impacts to ecological systems through levels of causal representation and scenario examination. Ultimately, Bayesian networks may facilitate understanding the factors contributing to ecological impacts, and the prediction and assessment of wildfire risks to ecosystems. Integr Environ Assess Manag 2021;17:1168–1178. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. KEY POINTS: The ladder of causation has broad implications for understanding the role of models in supporting assessment and decision‐making goals.Each of the rungs of the ladder is examined in terms of environmental assessment and decision models.Our case studies focus on managing wildfire risks for ecological endpoints but will be broadly applicable to other environmental issues.Establishing a causal hierarchy for environmental models will benefit future environmental assessment and management endeavors. [ABSTRACT FROM AUTHOR]

Being a kind of natural phenomenon, frost occurrence is influenced by environment factors with an accumulated impact. The relation between environment factors and frost event is of cause-effect governed by a process taking place in time. Having cause-affect concerned, frost forecast is a problem of complex cause-effect rather than a complicated association and problem modelling plays a key role for the success of forecast. With limited data and lack of true physical model, a well-trained model by machine learning from data is only an approximation constructed on a sub-space of problem domain. As a continued study of causal modelling in frost forecast developed previously, this paper proposes an ensemble causal modelling to compensate the performance of individual models. Such an ensemble involves models with different length of time-delay so to provide a spectrum of early alarm of frost occurrence. Experiments are done using sensor data collected from a vineyard in Hokkaido, Japan. [ABSTRACT FROM AUTHOR]

The paper deals with the causality perspective of the Enterprise Architecture (EA) frameworks. The analysis showed that there is a gap between the capabilities of EA frameworks and the behavioural characteristics of the real world domain (enterprise management activities). The contribution of research is bridging the gap between enterprise domain knowledge and EA framework content by the integration of meta-models as part of EA structures. Meta-models that cover not only simple process flows, but also business behaviour, i.e. causality of the domain, have been developed. Meta-models enable to create a layer of knowledge in the EA framework, which ensures smart EA development, allows validation of developer decisions. Two levels of the enterprise causal modelling were obtained. The first level uses the Management Transaction (MT) framework. At the second level, deep knowledge was revealed using a framework called the Elementary Management Cycle (EMC). These two causal frameworks were applied here to justify the causal meta-models of the EA. The new concepts Collapsed Capability, Capability Type and Capability Role which meaningfully complement MODAF with causal knowledge are introduced. Strategic Viewpoint (StV) modelling using causal meta-models is described in detail and illustrated in the case study. The example provided shows a principled way that causal knowledge supports the verification and validation of EA solutions. The presented method provides an opportunity to move the EA development to smart platforms. [ABSTRACT FROM AUTHOR]

I appraise some areas of recent achievement in economic methodology by identifying four topics on which there will likely be heavy exogenously generated demand for methodological innovation over coming years, and asking what foundations have been set for this work. The topics in question are economists' role in policy formation, macroeconomic management, causal and structural modeling of economic processes, and welfare with non-standard and dynamic utility. [ABSTRACT FROM AUTHOR]

Runway excursion (RE) is recognized as one of the main Airport Safety Issues. It can be defined as an event in which an aircraft veers-off or overruns the runway surface during either takeoff or landing. In this paper an application of Bayesian Belief Network (BBN) method on RE events is presented. BBN belongs to quantitative class of causal methods. It estimates the risk of incident or accident according to estimation of probability of occurrence of each cause of an event. It might be restricted to pure statistical analysis based on the available data or combine these data with expert judgment (personal beliefs) on the accident causes. The aim of this paper is to identify potential causal factors, analyze and determine the probability of their realization. In order to illustrate BBN application on RE, two models were developed: one for landing and other for take-off. Based on the Ishikawa diagram method, causal factors have been identified, and then through a qualitative BBN model, their interdependencies are presented. The quantification of the model is accomplished by combining statistical data with the expert beliefs. Sensitivity analysis has shown what are the most critical causal factors whose knowledge allows developing certain measures to reduce the risk of RE.

The Agile approach and tools are popular for the management of Enterprise Application Software (EAS) development. This article focuses on the issue of inconsistency between strategic business objectives and the functionality of the software developed. Agile management tools lack the functionality of EAS project activities coordination. This article aims to rethink Agile project management using the causal modelling approach. A causal model of Agile project management using a management transaction (MT) concept was developed. The notion of the space of processes was used to identify the MTs location along the axes of aggregation, generalization, and time and to formalize their interaction specifications. Taxonomy of the coordination meta-types and types was developed using the identifiers of the MTs. The modified Agile activities hierarchy was developed, and vertical and horizontal causal interactions between Agile activities were identified. This modified Agile management model helps to consistently track the integrity of EAS project content. Complexity indicators were introduced to evaluate the EAS project complexity and their average and normalized values are presented. Additional attributes in the Agile management tool Jira are proposed. Monitoring mismatch between strategic business objectives and development activities content helps to improve the success of EAS projects delivery.

Research Questions: Does being in the social welfare slow-onset disaster quadrant help in garnering resources during the sudden disaster? Theory: Humanitarian supply chain has been traditionally explained as a system involved in mobilizing people, resources, skills and knowledge for disaster relief operations. The established classification of disasters includes natural and man-made disasters. These are further classified into sudden- and slow-onset disasters. Social welfare supply chains happen to be a distinctive type of humanitarian supply chain working in slow-onset disasters such as poverty and drought. To understand the complex systems like humanitarian supply chain, system dynamics modelling is used. Type of the Case: Study of a phenomenon. Basis of the Case: In this study, we proposed a humanitarian supply chain case and system dynamics model that works as a social welfare supply chain. In the face of a calamity, operations are ramped up for the sudden-onset conditions. After the initial phase is over, operations are ramped down and again become stable. Protagonist: Absent. Findings: The study tests the results of four policy measures (a) increasing goodwill, (b) decreasing stringent directive, (c) increasing donor attitude and (d) a combination of all of these measures. These measures are additive in nature, and a humanitarian supply chain can use these policy measures to respond to a sudden disaster. Discussions: The case discusses various policy measures taken by the humanitarian supply chain during a sudden disaster. It also explores whether being in the slow-onset quadrant helps tack sudden disasters like earthquakes or terrorist attacks. An organization can start with decreasing the stringent measures in the case of a sudden disaster. Increasing donor attitude and goodwill requires long-term outreach efforts. [ABSTRACT FROM AUTHOR]

Summary: A common problem in educational evaluation is estimating causal effects of interventions from non‐experimental data on students. Scores from standardized achievement tests often are used to adjust for differences in background characteristics of students in different non‐experimental groups. An open question is whether, and how, these adjustments should account for the errors in test scores as measures of latent achievement. The answer depends on what information was used to assign students to non‐experimental groups. Using a case‐study of estimating teacher effects on student achievement, we develop two novel empirical tests about what information is used to assign students to teachers. We demonstrate that assignments are influenced by both information that is unobserved by the researcher, and error prone test scores. We develop a model that is appropriate for this complex selection mechanism and compare its results with common simpler estimators. We discuss implications for the broader problem of causal modelling with error prone confounders. [ABSTRACT FROM AUTHOR]

Background: Psychiatric disorders have been associated with higher risk for future dementia. Understanding how pre-dementia psychiatric disorders (PDPD) relate to established dementia genetic risks has implications for dementia prevention., Methods: In this retrospective cohort study, we investigated the relationships between polygenic risk scores for Alzheimer's disease (AD PRS), PDPD, alcohol use disorder (AUD), and subsequent dementia in the UK Biobank (UKB) and tested whether the relationships are consistent with different causal models., Findings: Among 502,408 participants, 9352 had dementia. As expected, AD PRS was associated with greater risk for dementia (odds ratio (OR) 1.62, 95% confidence interval (CI), 1.59-1.65). A total of 94,237 participants had PDPD, of whom 2.6% (n = 2519) developed subsequent dementia, compared to 1.7% (n = 6833) of 407,871 participants without PDPD. Accordingly, PDPD were associated with 73% greater risk of incident dementia (OR 1.73, 1.65-1.83). Among dementia subtypes, the risk increase was 1.5-fold for AD (n = 3365) (OR 1.46, 1.34-1.59) and 2-fold for vascular dementia (VaD, n = 1823) (OR 2.08, 1.87-2.32). Our data indicated that PDPD were neither a dementia prodrome nor a mediator for AD PRS. Shared factors for both PDPD and dementia likely substantially account for the observed association, while a causal role of PDPD in dementia could not be excluded. AUD could be one of the shared causes for PDPD and dementia., Interpretation: Psychiatric diagnoses were associated with subsequent dementia in UKB participants, and the association is orthogonal to established dementia genetic risks. Investigating shared causes for psychiatric disorders and dementia would shed light on this dementia pathway., Funding: US NIH (K08AG054727)., Competing Interests: Declaration of interests Dr. Goate received research funding from NIH and JPB Foundation, consulting fees from Muna Therapeutics and Genentech, payment for lectures from Biogen, Alector, and Denali Therapeutics, stock options from Cognition Therapeutics., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Abstract Background A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. Methods The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. Results The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall). In addition to the substantial direct effect of this variable (-0.60), its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12), on the one hand, and hospital caseload, on the other, (-0.10) are of similar size. Conclusions The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of Structural Equation Modelling. The authors demonstrate the efficacy of this approach in identifying complex causal pathways that mediate the effects of a socio-economic baseline covariate on the hazard of death from colon cancer. The results show that this approach has the potential to shed light on a class of research questions which is of particular relevance in health research.

Highlights • Fuzzy cognitive map is used to improve Bayesian network capability in poor data issues. • We proposed a new migration method from FCM to BBN. • The proposed method extracts BBN parameters from FCM ones. • The proposed method is capable to model operational risk as a data-poor issue. Abstract A main concern of risk management in financial institutions is measurement of operational risk and its value at risk as a requirement of Basel II accord. Besides risk quantification, identifying causal mechanism leading to operational loss is necessary to plan risk mitigation activities. Bayesian belief networks (BBN) is a causal modelling method able to achieve both goals simultaneously. Eliciting BBN causal model and its parameters from expert knowledge is an alternative to data driven models in case of data scarcity. However, there is still a problem with parameter extraction for complex models with a number of multi parent and multi state nodes. In this paper, we proposed a method combining fuzzy cognitive maps (FCM) and BBN in order to improve BBN capability in modelling operational risks. In the first phase, a causal model is constructed by applying FCM and then a new migration method is proposed to translate FCM parameters to BBN ones. A case study of an Iranian private bank is then given to examine and validate the proposed method. [ABSTRACT FROM AUTHOR]

Background: This study investigates the causal relationships between reading and print exposure and investigates whether the amount children read outside school determines how well they read, or vice versa. Previous findings from behavioural studies suggest that reading predicts print exposure. Here, we use twin‐data and apply the behaviour‐genetic approach of direction of causality modelling, suggested by Heath et al. (), to investigate the causal relationships between these two traits. Method: Partial data were available for a large sample of twin children (N = 11,559) and 262 siblings, all enrolled in the Netherlands Twin Register. Children were assessed around 7.5 years of age. Mothers completed questionnaires reporting children's time spent on reading activities and reading ability. Additional information on reading ability was available through teacher ratings and performance on national reading tests. For siblings reading test, results were available. Results: The reading ability of the twins was comparable to that of the siblings and national norms, showing that twin findings can be generalized to the population. A measurement model was specified with two latent variables, Reading Ability and Print Exposure, which correlated.41. Heritability analyses showed that Reading Ability was highly heritable, while genetic and environmental influences were equally important for Print Exposure. We exploited the fact that the two constructs differ in genetic architecture and fitted direction of causality models. The results supported a causal relationship running from Reading Ability to Print Exposure. Conclusions: How much and how well children read are moderately correlated. Individual differences in print exposure are less heritable than individual differences in reading ability. Importantly, the present results suggest that it is the children's reading ability that determines how much they choose to read, rather than vice versa. [ABSTRACT FROM AUTHOR]

Abstract: The Guidelines Challenge Conference on which this special issue builds asked as the first of its “further relevant questions”: “How do we incorporate more types of causally relevant information in guidelines?” This paper first supports the presupposition of this question—that we need further kinds of evidence—by pointing out that the randomized controlled trial, touted as the best source of evidence on effectiveness, can do so little for us. Second, it outlines a number of other good ways to learn what will work that the medical community, and much of the public health community, is not making much use of. [ABSTRACT FROM AUTHOR]

Based on the examination of the transactions made in 58 case study projects, we have developed probabilistic causation models that include relationships hypothesised from exhaustive literature reviews. These models contain relationships that relate a number of significant project variables to transport infrastructure project performance. Here, we report on the use of the Importance Analysis approach to identify the most significant factors linked to variables measuring project performance. Such an approach is used in combination of Bayesian Networks and Sensitivity Analysis. Some variables that resulted important to achieve cost, time, and revenue expectations in transport infrastructure projects are identified. These include factors other than those related to project governance but linked to the funding and financing schemes in a project and its context of implementation. Additionally, we analysed how projects in the BENEFIT database responded to the effects of the European economic crisis in 2008. The results indicated that some actions were implemented at some instances during the crisis time. Specific factors that appeared to be sufficiently robust to face the economic crisis were found. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goal (SDG) target for access to safe sanitation and hygiene represents a marked improvement over the target used during the Millennium Development Goal (MDG) period. The SDG target attempts to: explicitly address hygiene; eliminate inequalities within populations; evaluate sanitation services beyond the household; account for the accessibility, safety, acceptability, and affordability of service delivery; and improve the sustainability of services (WHO/UNICEF, 2015). However, the proposed indicators for monitoring progress in sanitation and hygiene still rely primarily on infrequent household surveys and census data. This paper provides a critical review of the sanitation and hygiene target and explores the potential gaps between the expanded understanding of access, the proposed monitoring strategies, and the desired impacts. A variety of innovative methodologies and technologies are reviewed, with specific attention given to their suitability for measuring and monitoring progress towards the sanitation and hygiene target. [ABSTRACT FROM AUTHOR]

It has become a popular view among non-reductive physicalists that it is possible to devise empirical tests generating evidence for the causal efficacy of the mental, whereby the exclusion worries that have haunted the position of non-reductive physicalism for decades can be dissolved once and for all. This paper aims to show that these evidentialist hopes are vain. I argue that, if the mental is taken to supervene non-reductively on the physical, there cannot exist empirical evidence for its causal efficacy. While causal structures without non-reductive supervenience relations can be conclusively identified in ideal discovery circumstances, it is impossible, in principle, to generate evidence that would favour models with mental causation over models without. Ascribing causal efficacy to the mental, for the non-reductive physicalist, is a modelling choice that must be made on the basis of metaphysical background theories or pragmatic maxims guiding the selection among empirically indistinguishable models. [ABSTRACT FROM AUTHOR]

Subject--object relations reflect the relation of phenomenology and physics and are at the centre of interest in brain research and neuro-psychology. The unresolved dichotomy behind this relation is one of the most challenging questions of our time. Setting out from causal modelling I suggest a particular topology for subject--object relations and argue that we can find a physical realization in living organism that provides a continuous transform between both domains. In a geometrical metaphor this transform has the topological properties of a one-sided surface or non-orientable flat. I argue that such a surface can be found within the electronic organization of atomic linings in the filter region of ion-conducting membrane proteins. Electron transfer along these atomic surfaces makes chiral induced spin changes to a promising signature of subject--object relations and has found experimental evidence in previous studies. I finally advocate the view that there is a basic dualism between subject and object which is physical on both sides and realized by an inversion relation along one-sided surfaces. The transition between these two aspects however is non-physical and hosts the phenomenology that characterizes subjectivity. [ABSTRACT FROM AUTHOR]

This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context—the Causal Markov Condition and Minimality—do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways when the joint probability distribution over those variables is imprecise, none of which provides a compelling basis for the causal interpretation of imprecise Bayes nets. I conclude that there are serious limits to the use of imprecise Bayesian networks to represent causal structure.

Identifying the environmental features affecting gene flow across a species range is of extreme importance for conservation planning. We investigated the genetic structure of the Egyptian mongoose (Herpestes ichneumon) in Western Iberian Peninsula by analyzing the correlations between genetic distances and landscape resistance models. We evaluated several functional relationships between elevation, vegetation cover, temperature, and genetic differentiation under the original and reciprocal causal modelling approaches. Additionally, we assessed evidence of isolation-by-distance (IBD) in the mongoose population. Original causal modelling identified IBD as the best model explaining genetic patterns in the mongoose population. By contrast, reciprocal causal modelling supported high shrub cover at middle elevations as the best model explaining species gene flow. The results from reciprocal causal modelling demonstrate that the Egyptian mongoose is dependent of ecosystems dominated by Mediterranean shrub cover. Recent land-use changes related to rural abandonment promoted the growth of shrub areas, especially at middle elevations, facilitating genetic connectivity in the mongoose population in those areas, where anthropogenic activities are less intense. The present study should be considered as a model for landscape genetics studies of Mediterranean carnivores in the Iberian range with the aim of better understanding how recent land-use changes affect a broad guild of species. [ABSTRACT FROM AUTHOR]

Key risk indicators (KRIs) are intended to track operational risk exposure and provide early indications of potential severe losses. Guidance on establishing the most effective KRIs for financial institutions is, however, limited and, as a result, KRIs are typically derived from an institution's available metrics, often leading institutions to compensate for a lack of effective KRIs by increasing the number of KRIs monitored. Strengthening the ability to identify and evaluate KRIs' effectiveness could increase the value of each KRI, further reducing the number of KRIs necessary and increasing the overall value of institutions' KRI framework. This paper proposes a theoretical foundation and method for identifying and evaluating effective KRIs. The proposed solution originates from research on causal analysis of operational risk, particularly using Bayesian networks. It was found that high-frequency and tail events can be related to a shared set of causes which can be exploited for the identification and evaluation of two categories of KRIs: (1) shared causes that constitute major risk drivers; and (2) high-frequency events providing a strong indication of changes in exposure to low-frequency, high-severity events. Applying the suggested method, financial institutions can map and evaluate current and potential KRIs, ensuring reliable monitoring of the operational risk exposure level. [ABSTRACT FROM AUTHOR]