Causal Analysis

A decision framework for setting management goals for species at risk is presented. Species at risk are those whose potential future rarity is of concern. Listing these species as threatened or endangered could potentially result in significant restrictions to activities in resource management areas in order to maintain those species. The decision framework, designed to foster proactive management, has nine steps: identify species at risk on and near the management area, describe available information and potential information gaps for each species, determine the potential distribution of species and their habitat, select metrics for describing species status, assess the status of local population or metapopulation, conduct threat assessment, set and prioritize management goals, develop species management plans, and develop criteria for ending special species management where possible. This framework will aid resource managers in setting management goals that minimally impact human activities while reducing the likelihood that species at risk will become rare in the near future. The management areas in many of the examples are United States (US) military installations, which are concerned about potential restrictions to military training capacity if species at risk become regulated under the US Endangered Species Act. The benefits of the proactive management set forth in this formal decision framework are that it is impartial, provides a clear procedure, calls for identification of causal relationships that may not be obvious, provides a way to target the most urgent needs, reduces costs, enhances public confidence, and, most importantly, decreases the chance of species becoming more rare.

Qualitative simulation (QS) is an area of artificial intelligence that represent continuos and discrete aspects like space, time and quantity with little information, and makes inferences using symbolic data to represent physical quantities. Tradicionally, QS uses a global statebased representation to represent the behavior of the system. To qualitative simulate a system, some initial values are normally given, along with qualitative differential equations (QDEs). With this information, a qualitative simulation algorithm evaluates all possible combinations of qualitative values and filters out inconsistent states considering qualitatives constraints. This is a combinational process which normally requires exponential time. In this paper, a new algorithm is described which simulates individual components independently and joins their behavioral graphs together until a global behavioral graph is obtained. This algorithm achieves substantial reductions in time and it is polynomial with respect to the number of components. It is shown how with this algorithm is possible to simulate industrial plants of hundreds of variables within a few minutes.

This paper is the first attempt to investigate the causal relationship between military spending, terrorist attacks and intensity of terrorism in Pakistan, by applying ARDL approach to cointegration and Innovation Accounting approach for causality analysis. The results indicate that war on terror is the major determinant of military spending followed by terrorism intensity and the number of terrorist attacks respectively. The study further finds that terrorism intensity and terrorist attacks Granger-cause military spending but the reverse causality is found absent. The failure of military measures to curtail terrorism and its intensity induces one to suggest greater involvement of civil intelligence agencies by raising their budgets instead of pure military budget.

Zusammenfassung Nach einer verbreiteten Auffassung hat die Transformation der EU zu einem Regime mit supranationalem Charakter, die durch den Vertrag von Maastricht eingeleitet wurde, mehrere legitimationswirksame Konsequenzen. Erstens führt sie zu einer Politisierung der EU, und in Folge davon wird die EU nicht mehr nur mit ökonomischen Effizienzkriterien, sondern auch mit demokratischen Standards konfrontiert. Zweitens wird den Bürgern auf diese Weise das Demokratiedefizit der EU bewusst, und das führt wiederum zu einer Erosion der Unterstützung der EU. Diese Thesen können durch die empirischen Befunde der Analyse nicht bestätigt werden. Nach dem Vertrag von Maastricht ist die Zufriedenheit mit dem Funktionieren der EU keineswegs gesunken, und die Kausalanalyse hat gezeigt, dass die EU immer noch primär auf der Grundlage instrumenteller Bewertungsstandards beurteilt wird. Der Bezugspunkt für die demokratischen Standards der Bürger ist offenbar weiterhin der eigene Nationalstaat, und die Einschätzung der Demokratie der EU kann als eine Generalisierung der Zufriedenheit mit der Demokratie des eigenen Landes begriffen werden. Es kann allerdings gefragt werden, ob die EU dauerhaft auf eine Mobilisierung von Unterstützung auf der Grundlage demokratischer Standards verzichten kann. Diese Mobilisierung würde allerdings die Institutionalisierung einer europäischen Demokratie voraussetzen.

We formulate performance assessment as a problem of causal analysis and outline an approach based on the missing data principle for its solution. It is particularly relevant in the context of so-called league tables for educational, health-care and other public-service institutions. The proposed solution avoids comparisons of institutions that have substantially different clientele (intake).

One of the most important advances brought about by life course and event history studies is the use of parallel or interdependent processes as explaining factors in transition rate models. The purpose of this paper is to demonstrate a causal approach to the study of interrelated family events. Various types of interdependent processes are described first, followed by two event history perspectives: the 'system' and 'causal' approach. The authors assert that the causal approach is more appropriate from an analytical point of view as it provides a straightforward solution to simultaneity, cause-effect lags, and temporal shapes of effects. Based on comparative cross-national applications in West and East Germany, Canada, Latvia, and the Netherlands, we demonstrate the usefulness of the causal approach by analyzing two highly interdependent family processes: entry into marriage (for individuals who are in a consensual union) as the dependent process and first pregnancy/childbirth as the explaining one. Both statistical and theoretical explanations are explored emphasizing the need for conceptual reasoning. L'utilisation des processus interdépendants ou parallèles en tant que facteurs explicatifs dans des modèles des transitions aux quotients instantanés est une des contributions les plus importantes de l'analyse des biographies. Le but de cet article est d'appliquer une approche causale à l'analyse des événements familiaux interdépendants. L'étude présente une typologie de processus parallèles et deux perspectives de l'analyse des biographies: les approches 'systémique' et 'causale'. Les auteurs soutiennent que l'approche causale est plus appropriée du point de vue d'analyse. Elle offre une solution valable aux problèmes de simultanéité, les problèmes de décalage dans les intervalles entre la cause et l'effet, et, enfin, les problèmes des courbes temporelles modelées par les effets. L'utilité de cette approche causale est démontrée grâce à l'analyse de deux processus fortement interdépendants: la transition des unions consensuelles au mariage, en tant que variable dépendante, et la première grossesse/naissance en tant que variable explicative. Cette analyse est appliquée dans plusieurs pays : l'Allemagne de l'Est et de l'Ouest, le Canada, la Lettonie, et le Pays-Bas. Tant l'interprétation statistique que celle théorétique sont explorées afin de souligner le besoin d'un raisonnement conceptuel.

Work domain analysis (WDA) is an approach developed by Rasmussen (1985) for representing the structure of complex work environments. Many examples of the approach have surfaced in the literature, predominantly of physically coupled causal systems (e.g., process control). For causal systems, the environment is strongly constrained by the laws of nature. This approach can also be used for representing intentional systems (e.g., military command and control), although there is some controversy on this issue. For intentional systems, the environment is strongly constrained by actors' intentions, values, and priorities of practice. This paper discusses the differences between causal and intentional systems and provides direction on how to proceed with a WDA for intentional systems. A WDA is presented for emergency ambulance dispatch management and military command and control to illustrate the approach. Finally, a discussion of the implications and future research recommendations are...

This study addresses the inadequacy of conventional failure analyses, which, in addition to regulatory and customer requirements,
often neglect organizational needs. It emphasizes the importance of a systemic approach to mitigating hazards in complex space
program management. This article proposes a new approach to addressing security issues that adds the management of security related
organizational needs to systemic engineering analysis. The case study of the catastrophic event involving SpaceX’s Starship
SN10 prototype used publicly available information to build the system-theoretic accident model and processes (STAMP) model
and identify organizational needs. The causal analysis based on systems theory (CAST) method was then applied to identify
possible causes. Finally, the system-theoretic process analysis (STPA) method was used to determine design-related organizational
needs and formulate recommendations for the design of the autogenous pressurization system. The presented method considered
organizational needs to identify the key elements involved in the accident, the primary causes, and the actions to mitigate the
associated hazards. This study proposed that managing organizational needs for system safety requires recognizing the current
situation and constructing prospective scenarios to prevent failures while emphasizing the importance of management’s proactive
measures, clear responsibilities, and active involvement of all members to ensure system reliability.

This paper presents a diagnostic methodology relying on a set-membership approach for fault detection and on a causal model for fault isolation. Set-membership methods are a promising approach to fault detection because they take into account a priori knowledge of model uncertainties and measurement errors. Every uncertain model parameter and/or measurement is represented by a bounded variable. In this paper, detection consists of verifying the membership of measurements to an interval. First order discrete time models are used and their output is explicitly computed with interval arithmetic. Fault isolation relies on a causal analysis and the exoneration principle, which allows focusing the consistency tests on simple local models. The isolation strategy consists of two steps: performing minimal tests found with the causal graph and determining on line additional relevant tests that reduce the final diagnosis. An application for a nuclear process is used in order to illustrate the method's efficiency.

This paper, empirically, tests the validity of Okun's law in Nigerian economy from 1980-2012. The two versions of the difference model approach of the Okun's law are used even though one of them is frequently used in the literature. We utilize Var-cointegration method and examine the direction of causality using the Var Granger causality/Block Exgeneity Wald test. We find that

This article reviews the literature on the onset and dynamics of domestic terrorism, with special emphasis on the interactions between terrorist organizations, the state, and society. Because this literature has often been based on case studies, we seek to impose some structure to its findings. We challenge the distinction between domestic and international terrorism, which truncates the sample of violence, and we show that the actor-sense of terrorism (violence carried out by underground organizations) is the most appropriate model for causal analysis. Terrorist organizations tend to emerge in developed countries in which the state is able to prevent the loss of control over any part of its territory. Terrorists take advantage of the state's mistakes (when, for example, it is over-repressive or makes ineffective concessions) in order to boost their support. Terrorists cannot survive without some degree of support. Consequently, levels of violence and targets are determined by s...

The mechanisms underlying the relationship between RR interval and systolic arterial pressure (SAP) variability at the respiratory frequency are still object of discussion. In this study, the information on directionality provided by causal cross-spectral analysis was exploited to infer possible influences of respiration on cardiovascular parameters variability. The ability of causal analysis to account for directionality in RR-SAP interrelationships in presence of respiratory exogenous effects was first tested on model simulations. Hence, real data measured on healthy subjects during spontaneous and paced breathing at 0.25 Hz were analysed. The results obtained in real data were consistent with simulations, thus supporting the hypothesis of different influences of respiration on SAP and RR interval variability under different physiological conditions.

Living conditions have played a special role in most of the former communist societies. After World War II, living conditions were controlled by the communist equality principle, while in the following decades living conditions improved and took on the role of legitimating the communist system. In this article we investigate the inequalities in living conditions in six post-communist societies (Bulgaria, Czech Republic, Hungary, Poland, Russia, and Slovakia). The data are taken from the 1993 comparative survey Social Stratification in Eastern Europe after 1989, conducted by Iván Szelényi and Don Treiman. Living conditions are operationalised from the viewpoint of material and cultural consumption. Separate measurements were constructed for both of these dimensions and present a typology of living conditions: (1) high material and high cultural living conditions; (2) high material and low cultural living conditions; (3) low material and high cultural living conditions; and (4) low material and low cultural living conditions. A causal analysis is performed where to investigate the social determination of belonging to different types of living conditions, applying multinominal logistic regression. Social and demographic characteristics of respondents (occupation, education, income, informal economy participation, gender, age, residence) and also 'accumulated' social assets from 1988 such as informal economy participation and party membership are used as predictor variables. Analyses are run separately for the six countries for presenting the country specific differences in the determination of living conditions.

Reliably identifying the causal factors underlying youth smoking initiation is an important part of developing effective smoking prevention programs and shaping other types of smoking‐related policies. The establishment of reliable scientific evidence in support of a causal link between cigarette advertising and youth smoking initiation depends on both rich longitudinal data as well as careful empirical applications. We examine basic principles of empirical scientific investigation of potential causal relationships, discuss findings of recent research on causal factors of youth smoking, and evaluate evidence from the public health literature regarding the effects of cigarette advertising on youth smoking. (JEL I18, C5)

The present study explores the relationship between economic growth, electricity consumption, urbanization and environmental degradation in case of United Arab Emirates. The study covers the quarter frequency data over the period of 1975-2011. We have applied the ARDL bounds testing approach to examine the long run relationship between the variables in the presence of structural breaks. The VECM Granger causality is applied to investigate the direction of causal relationship between the variables. Our empirical exercise reported the existence of cointegration among the series in case of United Arab Emirates. Further, we found an inverted U-shaped relationship between economic growth and CO 2 emissions i.e. economic growth raises energy emissions initially and declines it after a threshold point of income per capita (EKC exists). Electricity consumption declines CO 2 emissions. The relationship between urbanization and CO 2 emissions is positive. Exports seem to improve the environmental quality by lowering CO 2 emissions in case of UAE. The causality analysis validates the feedback effect between CO 2 emissions and electricity consumption. Economic growth and urbanization Granger cause CO 2 emissions.

The study attempts to analyze the static and dynamic causality between producers’ prices measured by wholesale price index (WPI) and consumers’ prices measured by consumers’ price index (CPI) in case of India. In doing so, we have applied the autoregressive distributed lag (ARDL) bounds testing approach to co-integration and robustness of the ARDL approach which is examined by Johansen and Juselius’ ( 1990 ) maximum likelihood approach over the time period of 1950–2009. We found bidirectional causality between producers’ and consumers’ prices in Indian economy. Variance decomposition (VD) forecast analysis reveals that in India, producers’ prices lead consumers’ prices. This implies that WPI is determined by market forces and also a leading indicator of consumers’ prices and inflation. This gives an indication to the Indian policy analysts to control for factors affecting WPI in order to have control on CPI since CPI is used for indexation purposes for many wage and salary earners i...

The article investigates the effect of financial development, imports and foreign direct investment (FDI) on economic growth in case of Pakistan over the period of 1990–2008 using quarterly data set. The Autoregressive Distributed Lag (ARDL) bounds testing approach is applied to examine the long-run relationship and the direction of causality is investigated using the Vector Error Correction Model (VECM) framework between the variables. Our findings confirm the existence of cointegration, showing long-run relation between financial development, imports, FDI and economic growth. Financial development, imports and FDI have a positive and significant effect on economic growth of the country. Causality analysis reveals a bidirectional relation among the variables but strong causality is also running from financial development, economic growth and FDI to real imports.

This study revisits the relationship between defence spending and economic growth using Keynesian model in Pakistan by applying ARDL bounds testing approach to cointegration for long run and error correction method for short span of time. Empirical evidence suggests a stable cointegration relationship between defence spending and economic growth. An increase in defence spending retards the pace of economic growth confirming the validation of Keynesian hypothesis in the country. Current economic growth is positively linked with economic growth in previous period while rise in nonmilitary expenditures boosts economic growth. Interest rate is inversely associated with economic growth. Finally, unidirectional causality running from military spending to economic growth is found.

The paper empirically analyzes, in Romania's case, the cointegration and causality between electricity consumption, capital and economic growth. The data set covers the period 1980-2011. The results show the existence of bidirectional causality between electricity consumption and economic growth and between economic growth and capital use. At the same time, a unidirectional causal relation is also found from capital use to electricity consumption. The main finding suggests that electricity conservation policies may hinder economic growth by reduction in electricity consumption. Moreover, in the opposite direction, from economic growth to electricity consumption, the fluctuations in economic growth may reduce demand for electricity.

This paper detects the direction of causality among carbon dioxide (CO 2) emissions, energy consumption, and economic growth in Next 11 countries for the period 1972-2013. Changes in economic, energy, and environmental policies as well as regulatory and technological advancement over time, cause changes in the relationship among the variables. We use a novel approach i.e. time-varying Granger causality and find that economic growth is the cause of CO 2 emissions in Bangladesh and Egypt. Economic growth causes energy consumption in the Philippines, Turkey, and Vietnam but the feedback effect exists between energy consumption and economic growth in South Korea. In the cases of Indonesia and Turkey, we find the unidirectional time-varying Granger causality running from economic growth to CO 2 emissions thus validates the existence of the Environmental Kuznets Curve hypothesis, which indicates that economic growth is achievable at the minimal cost of environment. The paper gives new insights for policy makers to attain sustainable economic growth while maintaining long-run environmental quality.

The induction of the well known and widespread phenomenon 'stomatal patchiness' has been attributed to a variety of potential causes: from low PPFD levels, all kinds of stress conditions to CO 2 -changes and even fungal infections. A mechanism which explains the occurrence of reproducible patterns of static (i.e. stable) stomatal patchiness at low PPFD levels is proposed. Further, experimental evidence is presented for the hypothesis that dynamic (i.e. not stable) stomatal patchiness is a consequence of heterogeneous water status in different parts of the leaf and can be induced by all ambient factors which cause such heterogeneities.

This study investigated the causal relationship between the working alliance and client outcomes in the client–case manager relationship. All 162 study participants received services from a case manager who worked as a member of an assertive community treatment team. All participants had both a substance use disorder and a diagnosis of severe mental illness and were homeless at baseline. A brief form of the Working Alliance Inventory (WAI) was used to measure the working alliance after 3 and 15 months of treatment. The Brief Psychiatric Rating Scale was used to measure psychiatric symptoms at baseline, 9, and 18 months. Days per month that clients used alcohol or illegal drugs was also assessed at baseline, 9, and 18 months. The data were analyzed using structural equation modeling (SEM). The correlations between the working alliance and the outcome measures were lower than similar correlations obtained in studies investigating the psychotherapy relationship. The SEM analyses reveal...

The Manufacturing Planning and Control (MPC) tools (such as MRP, MRP II, ERP, CIM, JIT, and TQM) have had always gained an important edge in the operations, and productions management of any industrial sector of the world whether dealing with the assembling operations or the manufacturing operations. The MPCs techniques are used essentially to achieve the lowest possible production cost with the eminent quality of products. For any Asian country such as Pakistan whose economy is mostly dependent upon the industry, requires lucrative success and a strong position across the globe by adopting and implementing the nouveau MPC tools. This paper investigates the MPC systems implementation achieved so far within the SMEs of Pakistan and also forecasts the MPC tools future in Pakistan's industry by using the questionnaire methodology (i.e. survey approach).

Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combined with structured EHR data such as patients' demographics, diagnoses, and medications. This will enhance healthcare providers' ability to identify aspects of a patient's story communicated in the clinical notes and help make more informed decisions. In this work, we propose annotation guidelines, develop an annotated corpus 1 and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts in clinical notes; communicated implicitly or explicitly, identified either in a single sentence or across multiple sentences. We annotate a total of 2714 de-identified examples sampled from the 2018 n2c2 shared task dataset and train four different language model based architectures. Annotation based on our guidelines achieved a high inter-annotator agreement i.e. Fleiss' kappa (κ) score of 0.72, and our model for identification of causal relations achieved a macro F1 score of 0.56 on the test data. The high inter-annotator agreement for clinical text shows the quality of our annotation guidelines while the provided baseline F1 score sets the direction for future research towards understanding narratives in clinical texts. * Corresponding Author † Equal contribution ‡ Contributed during an internship at Accenture labs, SF 1 MIMICause dataset will be available under the "Community Annotations Downloads" at https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/

To ensure a continuous and smooth work flow, this research proposes (1) Tact Scheduling Method as an alternative to traditional ones and (2) Work Flow Monitoring System (WFMS). Tact scheduling is intended to balance the unit activities of relevant work trades in terms of the duration and the labor input required for performing each activity. Subcontractors as well as the main contractor participate in the planning stage and adjust the same duration and the labor input for every unit activity by zoning the workspace. Prior to developing the WFMS, a few factors that are considered to possibly affect the variation of the work flow are defined and indicators to measure these factors are devised.

This paper describes a relationship between workflow and labor factors such as variations in labor resource stability and worker loyalty. In order to measure such factors, relevant indicators are identified and validated through interviews with experts. In addition, a causal model is presented to explain the relationship among labor factors and workflow. The theoretical basis for this model rests on the assumption that worker loyalty and the rate of worker change are consistent with variations in the planned workflow. A case study of a building construction project was performed to implement the causal model. A statistical method, called path analysis, was adopted to verify the causal model. The result of the analysis is in good agreement with the concepts used in the assumption, and thus, the conclusion can be made that the suggested labor factors are substantial in the management of workflow.

This paper describes a relationship between workflow and labor factors such as variations in labor resource stability and worker loyalty. In order to measure such factors, relevant indicators are identified and validated through interviews with experts. In addition, a causal model is presented to explain the relationship among labor factors and workflow. The theoretical basis for this model rests on the assumption that worker loyalty and the rate of worker change are consistent with variations in the planned workflow. A case study of a building construction project was performed to implement the causal model. A statistical method, called path analysis, was adopted to verify the causal model. The result of the analysis is in good agreement with the concepts used in the assumption, and thus, the conclusion can be made that the suggested labor factors are substantial in the management of workflow.

Ré sumé Contrô ler les variables dans les systè mes sociaux-Une approche de modé lisation structurelle. En sciences sociales, lors de l'étude de relations causales, il n'est en général pas évident de choisir les variables à contrô ler. En recourant à une approche structurelle et aux graphes acycliques orientés, un ensemble cohérent de lignes directrices est proposé pour déterminer les variables devant être contrô lées. Deux règles sont établies pour l'analyse des effects directs et des effets totaux d'une variable sur une autre. Ces règles sont appliquées, à titre d'exemple, à l'usage de la contraception dans quatre grandes villes africaines. Cette approche, permettant de déterminer les variables à contrô ler, est plus simple et plus complète que d'autres approches basées sur le critère du back-door de Pearl.

Background: Cognitive impairment (i.e. lower IQ/cognitive development) in individuals who have experienced childhood maltreatment is well documented in the literature. It is not yet clear whether maltreatment itself causes cognitive impairment, or whether reduced cognitive functioning pre-dates maltreatment exposure and places children at risk of maltreatment. Objective: This systematic review critically evaluated the evidence for a causal association between child maltreatment and impaired cognition in children under 12 years. Methods: Following PRISMA guidelines, databases were searched and articles extracted according to inclusion criteria. Quality rating of articles was conducted independently by two reviewers and the evidence for a causal association was evaluated using guidelines based on the Hill criteria for causation in epidemiological and public health research. Results: 31 articles were included in the review, with results that suggested lower IQ/cognitive development in maltreated children compared to controls, and a dose-response relationship between timing and duration of maltreatment and impaired cognition. Assessment of causality indicated strong evidence for a causal association between maltreatment and reduced overall cognitive performance in institutionalised children. Findings were less robust for non-institutionalised samples. Evidence regarding specific cognitive functions was mixed. Conclusions: Extreme maltreatment may lead to reduced cognitive functioning in children under 12 years. More research is required to determine the impact of the nature and timing of maltreatment, as well as additional heritable and social factors, on specific profiles of cognition in this population.

Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combined with structured EHR data such as patients' demographics, diagnoses, and medications. This will enhance healthcare providers' ability to identify aspects of a patient's story communicated in the clinical notes and help make more informed decisions.

As a result of the rapid growth of microeconometric studies of exporting firms, we now know quite a lot about the performance dynamics of firms that enter export markets. We know much less about what happens to performance when firms exit. We apply a difference-indifferences methodology based on matched firms and analyse the performance dynamics of firms in UK manufacturing that exited export markets over the period 1991-1997. We find that on average, exit from foreign markets has a negative albeit weak effect on total factor productivity, but this is confined to the year of exit as we fail to detect any discernible productivity effect due to exit in subsequent years. By contrast, exit is found to have a deleterious effect on both employment and output dynamics. The effect on output is sizeable and quite persistent, suggesting that domestic demand was not able to make up for the loss in foreign market shares.

We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this paper, we combine these two parts. For each DAG in the estimated equivalence class, we use intervention calculus to estimate the causal effects of the covariates on the response. This yields a collection of estimated causal effects for each covariate. We show that the distinct values in this set can be consistently estimated by an algorithm that uses only local information of the graph. This local approach is computationally fast and feasible in high-dimensional problems. We propose to use summary measures of the set of possible causal effects to determine variable importance. In particular, we use the minimum absolute value of this set, since that is a lower bound on the size of the causal effect. We demonstrate the merits of our methods in a simulation study and on a data set about riboflavin production.

This study aims to determine the influence of the linkage of the fund's village program, economic growth, inequality of income distribution, unemployment rate, and human development index on poverty alleviation in Indonesia in the short and long term. The method used in this research is to use the panel data regression and Generalized Methods of Moment (GMM). The data used is secondary data with a cross-section (provinces) and time series (years) data. The cross-section data used is from 33 provinces in Indonesia. The time series data used is annual data from 2017 to 2022. The results showed that the fund's village, unequal income distribution, and unemployment had a significant positive effect on poverty in Indonesia partially. In comparison, economic growth and the human development index had a significant negative effect on poverty. Poverty decreases in Indonesia as economic growth and HDI both rises. The interaction of the fund's village and the human development index, in contrast, has a negative and significant impact on the poverty rate, according to the findings of the GMM System. When fund's villages are used to build human resources, the poverty rate is reduced.

This article investigates causal relationships leading to emergence in an agent-based model of human behaviour. A new method based on nonlinear structural causality is formulated and practically demonstrated. The method is based on the concept of acausal partitionof a model variable which quantifies the contribution of various factors to its numerical value. Causal partitions make it possible to judge the relative importance of contributing factors over crucial early periods in which the emergent behaviour of a system begins to form. They can also serve as the predictors of emergence. The time-evolution of their predictive power and its distribution among their components hint at the deeper causes of emergence and the possibilities to control it.

In this paper, we introduce CR-COPEC called Causal Rationale of Corporate Performance Changes from financial reports. This is a comprehensive large-scale domain-adaptation causal sentence dataset to detect financial performance changes of corporate. CR-COPEC contributes to two major achievements. First, it detects causal rationale from 10-K annual reports of the U.S. companies, which contain experts' causal analysis following accounting standards in a formal manner. This dataset can be widely used by both individual investors and analysts as material information resources for investing and decision-making without tremendous effort to read through all the documents. Second, it carefully considers different characteristics which affect the financial performance of companies in twelve industries. As a result, CR-COPEC can distinguish causal sentences in various industries by taking unique narratives in each industry into consideration. We also provide an extensive analysis of how well CR-COPEC dataset is constructed and suited for classifying target sentences as causal ones with respect to industry characteristics. Our dataset and experimental codes are publicly available 1 .

A relevant question in social science is whether cognitive bias can be instigated by social pressure or is it just a rational reaction to incentives in place. Sport, and association football in particular, offers settings in which to gain insights into this question. In this paper we estimate the determinants of the length of time between referee appointments in Spanish soccer as a function of referee decisions in favour of the home and away team in the most recent match by means of a deep-learning model. This approach allows us to capture all interactions among a high-dimensional set of variables without the necessity of specifying them beforehand. Furthermore, deep-learning models are nowadays the state of the art among the predicting models which are needed and here used for estimating effects of a cause. We do not find strong evidence of an incentive scheme that counteracts well-known home referee biases. Our results also suggest that referees are incentivised to deliver a moder...

We begin this brief rejoinder by thanking all the authors who took time to provide comments on our work, which appeared recently in this journal (Aguirre-Urreta, . All commentaries appear to suggest that causal indicators cannot be used for measurement but differ in how strongly this conclusion is stated; commentaries challenging our work and its conclusions are notably absent. In what follows we outline some common themes across the commentaries, provide some thoughts on these themes, and outline areas we believe need further research. Whether a concept can be measured by observing items representing its causes is an important question in the causal indicator literature. This issue is addressed directly in the commentaries by and . Cadogan and Lee (2016) note that "measurement is about quantifying the properties of objects" (p. 162), which very closely mirrors our own views on the topic. The issue at hand is whether the procedures advocated in the causal indicator literature, as it currently stands (more on this later), allow us to "quantify the properties of objects." This is not currently the case. In particular, Cadogan and Lee (2016) outline the conditions that would need to be satisfied for a causal indicator model to provide information on the variance of the latent variable of interest; a researcher would need (a) to know the identity of all causes, (b) to know the form and magnitude of the paths relating each of those causes to the latent variable, and (c) to have directly observed data on the variances and covariances of those causes. While most articles on causal indicators do not explicitly explain whether it is the causal indicators (i.e. observed data) or the attribute that the causal indicator represents that acts as a cause, we argue that only this second interpretation of the literature is plausible. This interpretation necessitates adding a fourth item to the list: that the causal indicators are free from measurement error-that is, that all the variance in the causal indicators is relevant variance for the purpose of explaining the latent variable of interest. It is clear that researchers typically do not have access to all the necessary information and thus, in practice, it would be difficult to argue that causal indicators can measure the latent variable of interest. Moreover, though maybe not evident at first sight, if researchers were to be able to satisfy all those conditions, then we would no longer be talking about causal indicators but rather composite indicators (cf. Bollen & Bauldry, 2011) since having knowledge of all possible causes of the latent variable (and the relevant magnitudes of the paths, etc.) there would be no need for a disturbance term and the variable would not be latent but a composite of its observed causes. The nature of the disturbance term is, we believe, one of the unresolved issues in this literature (see below for more on this). From a similar perspective on whether the reflective and causal approaches can be considered to be alternative measurement schemas, Markus (2016) noted that our arguments and results provide an important

This article reviews the literature on the onset and dynamics of domestic terrorism, with special emphasis on the interactions between terrorist organizations, the state, and society. Because this literature has often been based on case studies, we seek to impose some structure to its findings. We challenge the distinction between domestic and international terrorism, which truncates the sample of violence, and we show that the actor-sense of terrorism (violence carried out by underground organizations) is the most appropriate model for causal analysis. Terrorist organizations tend to emerge in developed countries in which the state is able to prevent the loss of control over any part of its territory. Terrorists take advantage of the state's mistakes (when, for example, it is over-repressive or makes ineffective concessions) in order to boost their support. Terrorists cannot survive without some degree of support. Consequently, levels of violence and targets are determined by s...

Qualitative reasoning uses incomplete kmiowlcdge to conipute a description of the possible behaviors for dynamic systems. For complex systems containing a large number of variables and constraints, t.he simulation frequently is inti-actable or results in a large, incomprehensible behavioral description. Abstraction and aggm-egation techniques are reqiied during the simulation to eliminate irrelevant details and highlight the imnportant characteristics of the behavior. The total temporal ordering of unrelated events provided by a traditional state-based qualitative representatiomi is one such irrelevant distinction. Model decomposition and simulation addresses this probleni. Model decomposi ion uses a causal analysis of the model to partition the variables into tightly connected coatponents. The components are simulated separately in the order dictated by the causal analysis beginning with causally upstream connpomtents. Iniforniation from the simulation of causally upstream coniponents is used to constrain the behavior of downstreammm components. If a feedback loop exists between components or a set of components are acausally related, then a concurrent simulation is performed for these components. A truth maintenamicc system is used to record said retract assumuptions nuade during this concurrent simulation. Model decomposition provides a general architecture which separates the method of siniulation from the model decomposition algorithm. This architecture can he used to introduce alternative abstraction techniques to eli miiinate other irrelevant distinctions.

This paper is the first attempt to investigate the causal relationship between military spending, terrorist attacks and intensity of terrorism in Pakistan, by applying ARDL approach to cointegration and Innovation Accounting approach for causality analysis. The results indicate that war on terror is the major determinant of military spending followed by terrorism intensity and the number of terrorist attacks respectively. The study further finds that terrorism intensity and terrorist attacks Granger-cause military spending but the reverse causality is found absent. The failure of military measures to curtail terrorism and its intensity induces one to suggest greater involvement of civil intelligence agencies by raising their budgets instead of pure military budget.

The purpose of this paper is to perform an exploratory analysis of the causal interactions among global change drivers of regime shifts. Causal loops diagrams were used to collect a set of feedback mechanisms underlying abrupt change dynamics in 11 regime shifts. In order to prioritize drivers and to map out possible causal pathways we used network analysis. Agricultural processes, global warming, biodiversity loss, demographic and economic drivers are the main causes of regime shifts. Based on the analysis of 400 pathways, we intuitively suggest five types of cascading effects between regime shifts. Regime shifts dramatically affect the provision of ecosystem services and might undermine the achievement of the first Millennium Development Goal: reduction of hunger and poverty.

This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical thinking for inferring causal links between distinct cause and effect events, but here we assess whether this is also viable for causal thinking about continuous processes. Controlling for verbal and non-verbal ability, two studies (N = 107; N = 124) administered a battery of covariation, probability, spatial–temporal, and causal measures. Results indicated that spatial–temporal analysis was the best predictor of ca...

Is doing harm morally worse than allowing it to occur? Our everyday intuitions, supported by a long-standing tradition in moral philosophy, suggest that this is the case. Nonetheless, the study of framing effects and cognitive biases has pointed out that our intuitions over the doing/allowing distinction are far from robust and reliable. This line of research casts doubts over the adequacy of our intuitions in grounding the moral principle "doing is worse than allowing" and seems to downplay the doing/allowing distinction as a cognitive bias or as a byproduct of our flawed reasoning skills. In this paper, I take evidence about framing and biases as a serious threat to the doing/allowing distinction. However, if we aim to explain common-sense morality, we need to account for its widespread use. To keep these two insights together, I build a causal model of the distinction, based on Christopher Hitchcock's self-contained network account, which explains instances of attributions of these two labels. I conclude that the doing/allowing distinction can be better understood as a heuristic: in most cases, it helps us delivering quick moral judgements, but it can also misfire when cases are unfamiliar, underdescribed, or controversial.

The equation represents a weighted and scaled contrast between cell frequencies in the contingency table (as perceived by the reasoner) confirming (a, d) or disconfirming (b, c) the existence of a generative causal link between a candidate cause and an effect. References

Corrupt behavior presents major challenges for organizations in a wide range of settings. This article embraces a complexity theoretical perspective to elucidate the causal patterns of factors underlying consumers' unethical judgments. This study examines how causal conditions of four distinct domains combine into configurational causes of unethical judgments of two frequent forms of corrupt consumer behavior: shoplifting and fare dodging. The findings of fuzzy-set Qualitative Comparative Analyses indicate alternative, consistently sufficient ''recipes'' for the outcomes of interest. This study extends prior work on the topic by offering new insights into the interplay and the interconnected structures of multiple causal factors and by describing configurational causes of consumers' ethical evaluations of corrupt behaviors. This knowledge may support practitioners and policy makers to develop education and control approaches to thwart corrupt consumer behaviors.

Air traffic worldwide is increasing more and more in recent years. In 2017, a total of 4,1 billion passengers were carried. Never in history such a high number been achieved. Especially the rising population of socalled lowcost airlines have caused a veritable hype. Not only does this place an increasing burden on the environment, it also poses difficult tasks for the world’s airports. This crowd of people first has to be processed without causing a complete chaos. This need to think of ways to overcome this challenge has led us to explore socalled automated border systems. These service systems aim to accelerate immigration control and thus prevent longer waiting times. We describe the functioning as well as the advantages and disadvantages of the existing service systems. We collected, presented and analyzed of the data that were used for modeling. Main part of this paper is the creation of simple mathematical models. Both a single server and multi-server models are created at thr...

The paper is a joint review and presentation of the work titled, “Can Informed Public Deliberation Overcome Clientelism? Experimental Evidence from Benin” by Thomas Fujiwara and Leonard Wantchekon in a class of Causal Inference and Development Economics. The article is a review pivoted on the effect of clientelism in voting behaviour and vote turnout as submitted by the main authors; Thomas Fujiwara and Leonard Wantchekon.