Causal Mapping Research Articles

A survey of single nucleotide polymorphisms identified from whole-genome sequencing and their functional effect in the porcine genome.

Genetic variants detected from sequence have been used to successfully identify causal variants and map complex traits in several organisms. High and moderate impact variants, those expected to alter or disrupt the protein coded by a gene and those that regulate protein production, likely have a more significant effect on phenotypic variation than do other types of genetic variants. Hence, a comprehensive list of these functional variants would be of considerable interest in swine genomic studies, particularly those targeting fertility and production traits. Whole-genome sequence was obtained from 72 of the founders of an intensely phenotyped experimental swine herd at the U.S. Meat Animal Research Center (USMARC). These animals included all 24 of the founding boars (12 Duroc and 12 Landrace) and 48 Yorkshire-Landrace composite sows. Sequence reads were mapped to the Sscrofa10.2 genome build, resulting in a mean of 6.1 fold (×) coverage per genome. A total of 22342915 high confidence SNPs were identified from the sequenced genomes. These included 21million previously reported SNPs and 79% of the 62163 SNPs on the PorcineSNP60 BeadChip assay. Variation was detected in the coding sequence or untranslated regions (UTRs) of 87.8% of the genes in the porcine genome: loss-of-function variants were predicted in 504 genes, 10202 genes contained nonsynonymous variants, 10773 had variation in UTRs and 13010 genes contained synonymous variants. Approximately 139000 SNPs were classified as loss-of-function, nonsynonymous or regulatory, which suggests that over 99% of the variation detected in our pigs could potentially be ignored, allowing us to focus on a much smaller number of functional SNPs during future analyses.

Supply Chain Causal Linkage-based Strategic Map Design

Supply Chain Causal Linkage-based Strategic Map Design

Testing a Landsat-based approach for mapping disturbance causality in U.S. forests

In light of Earth's changing climate and growing human population, there is an urgent need to improve monitoring of natural and anthropogenic disturbances which effect forests' ability to sequester carbon and provide other ecosystem services. In this study, a two-step modeling approach was used to map the type and timing of forest disturbances occurring between 1984 and 2010 in ten Landsat scenes located in diverse forest systems of the conterminous U.S. In step one, Random Forest (RF) models were developed to predict the presence of five forest disturbance agents (conversion, fire, harvest, stress and wind) and stable (i.e. undisturbed) forest. Models were developed using a suite of predictors including spectral change metrics derived from a nonparametric shape-restricted spline fitting algorithm, as well as several topographic and biophysical variables which potentially influence the initiation and/or spread of forest disturbance agents. Step two involved applying a rule-based model to the spectrally-based shape parameters (e.g. shape type, year and duration) to assign a year to the disturbance types and locations predicted in step one. Out of bag (OOB) predictions from RF showed that across the ten scenes, overall agreement was highest when only causal agent was considered (avg=80%, min=69%, max=86%), and was lowest when both agent and year (within ±1 of the reference date) were required to be correct (avg=71%, min=56%, max=80%). Across scene omission and commission errors for fire and stable forest classes were mostly around 10% to 20%, respectively. Harvests were also modeled well, as five of nine test scenes had error rates <26%. Accuracy of the wind and stress classes were much more variable with model errors ranging from 24% to 88%. The years assigned by the rule-based model were reasonably accurate, as 88% of all disturbances were assigned a year that fell within ±2years of the reference date. Fire disturbances were assigned the correct year 78% of the time, followed by harvest (69%) and conversion (54%). Although 17% and 63% of wind and stress disturbances were under-estimated by 5 or more years, the impact on overall accuracy was nominal given these two classes only accounted for roughly 5% of all disturbances. Our results also revealed that causal agent models summarized to broader disturbed/not disturbed classes were as accurate as models specifically constructed to predict binary disturbance, thus there appears to be no advantage to modeling disturbance prior to assigning causality. A relative evaluation of mean decrease in accuracy from RF showed that although a wide range of predictor variables contributed to the successful modeling of causal agents and stable forest (e.g. patch metrics, forest occurrence, and topography), disturbance variables (e.g. MTBS) and spectral change metrics (e.g. absolute and relative magnitude) were by far the most important. Modeled causality maps and annual disturbance rates were examined and found to be in good agreement with existing literature and other published data sets. Lastly, results are used to make recommendations for mapping forest disturbance agents nationally across the U.S.

When it rains, it pours: A triple-pathway model of collective turnover based on causal mapping analysis

As modern organizations become increasingly knowledge intensive, collective turnover, a phenomenon of knowledge spillover or transfer, is gradually receiving greater attention from scholars. Given that most studies have focused on the detrimental organizational consequences of collective turnover, this paper proposes a relational perspective to explore three distinct process models of dispersed, translocated, and entrepreneurial collective turnover. Based on a causal mapping analysis conducted in China of 25 collective turnover events that can demonstrate complex turnover processes over time, this study found that dispersed collective turnover, in which a group of members quit and scatter, is more likely to be triggered by shared job dissatisfaction. While translocated collective turnover, in which organizational members collectively mobilize in the same organization, is highly associated with attractive external lures. In contrast, entrepreneurial collective turnover, in which a group of members leave to start a new business together, is developed through a group process of entrepreneurial passion contagion. The ensuing triple-pathway model helps to enrich the theoretical understanding of collective turnover by clarifying the contextual differences regarding the effects of turnover destination on the important process of collective turnover.

From vicious to virtuous circles: problem structuring for quantified decision making in operationalization of corporate social responsibility

There is no formalised approach for problem structuring and quantitative decision support to operationalise corporate social responsibility (CSR) implementation. In this paper, techniques for considering criteria relationships are outlined and a holistic, systematic framework combining a qualitative and quantitative method for practical CSR integration is provided. Cognitive mapping (CM) is applied to structure the problem picture, and the cause–effect relationships between decision elements. Soft CM methodology is employed to assess the cross-criteria interactions, at both an individual and a collective level. The interactions of criteria can have a significant impact upon CSR implementation. Such impacts can be direct or indirect through their close linkages to other criteria. The causal strategic map serves as an input to the analytic network process (ANP) to carry out the multi-criteria decision analysis. Then, CM and ANP are applied in a comparative analysis to verify whether the measures of criteria significance do correspond. The key criteria in networks are identified using centrality in CM and single limited priorities in ANP. This study demonstrates that using criteria without considering their interactions will result in shortcomings in the evaluation and assessment of CSR programmes. The holistic framework, combining CM and ANP proposed in this work, enhances the process of problem structuring and supports preference-based evaluation of decision alternatives. The results of our study yield that the mapping procedure has an influence on the criteria significance in networks. The correspondence between CM and ANP is stronger when cause relationships are rigidly interpreted. More unambiguous interpretations of causal relations can be achieved if methods are used jointly and common peaks of importance in both CM and ANP could potentially serve as indications of key decision elements.

Does Gender Equality Lead to Better-Performing Economies A Bayesian Causal Map Approach

This study explores the existence of relationships between gender inequalities –represented by the components of the World Economic Forum (WEF) Global Gender Gap Index– and the major macroeconomic indicators. The relationships within gender inequalities in education, the labour market, health and the political arena, and between gender inequalities and gross macroeconomic aggregates were modelled with the Bayesian Causal Map, an effective tool that is used to analyze cause-effect relations and conditional dependencies between variables. A data set of 128 countries during the period 2007–2011 is used. Findings reveal that some inequalities have high levels of interaction with each other. In addition, eradicating gender inequalities is found to be associated with better economic performance, mainly in the form of higher gross domestic product growth, investment, and competitiveness.

Understanding electronic health records resistance: a revealed causal mapping approach

The implementation of electronic health records (EHR) has potential benefits to healthcare organisations. However, evidence show that EHR systems are being resisted by healthcare providers. Little research in IS has addressed this phenomenon. To understand EHR resistance, a mid-range theory is evoked from our textual analysis of responses gathered from healthcare providers at a large international hospital. The data were analysed using revealed causal mapping technique (RCM). The results revealed that EHR resistance is comprised of seven major constructs: costs, benefits, perceived value, perceived threat, organisational support, self-efficacy, and system circumvention availability. Additionally, this study also uncovered the underlying concepts that are shaping each of these constructs. This study demonstrated that the use of the RCM methodology yielded concepts and constructs of EHR resistance that are not suggested by generalised theory. The use of RCM also revealed the main interactions and linkages between these constructs.

Understanding electronic health records resistance: a revealed causal mapping approach

Understanding electronic health records resistance: a revealed causal mapping approach

A systems tool for structuring public policy problems and design of policy options

Systems analysis allows quantitative empirical testing of models that exist in the study of public policy. Simulation and visualisation techniques can help policy makers to reduce uncertainties on the possible impacts of policies. This paper presents a new tool for systems modelling and simulation of policy problems to support the problem analysis, the design of policy options and impact assessment activities. The tool facilitates the cognitive activity of representing complex mental models using system dynamics simulation modelling by defining standards and a procedure for policy modelling. We propose a new policy-oriented problem structuring method (PSM), the 'labelled causal mapping'. The method supports scenario-based dynamic simulation and provides graphical representations of the involved actors, key variables, control flows and causal dependencies in a policy decision situation. A web-based tool prototype has been implemented in a Node.js environment. Two real policy problems are presented for demonstration of the use of the prototype.

THE USE OF CAUSAL MAPS AS INTERDISCIPLINARY DIDACTIC REDUCTION METHOD

Presented article is devoted to theoretical substantiation and development of interdisciplinary didactic reduction method, based on the use of causal maps. The article discusses properties of causal maps in the context of its use in teaching methods. It was determined that the creation of the causal maps that reflect all connections between and / or within the three information blocks should be an effective reduction method of learning content of the topics about measurements of the various sciences. The blocks are named: “The physical basis of measurement”, “The elements of the measuring instrument” and “The measurement technique”. The article establishes that the method of using of causal maps in related topics of natural sciences, engineering, social sciences and humanities corresponds to properties of didactic reduction methods, due to abstraction and providing the omission of factors that do not have a determining influence on the situation, and getting in the vertices concepts less volume than learning texts. The method can provide an illustrative or symbolic representation of complex information on causal maps, review situations based on known models. The research justifies the possibility of constructing of quantitative variables in formulas to their qualitative explanations and presentation of the relationship between them in the causal maps. The process of tasks’ decision, that has different difficulty levels, using causal maps allows to influence on additional perception channels, and through the students' understanding of causality allows intensifying the development of mental processes.

Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.

Designing Casual Maps Adopting an Intellectual Capital Perspective: A Case Study Analysis

Casual maps are a useful tool by which managers can understand what the key resources in their organization are and how these resources contribute to the value creation process. The aim of this paper is to understand, from an empirical perspective, if, how and why a causal map can be designed grounding on the Intellectual Capital (IC) studies. To achieve this aim a case study has been examined to reply to our research questions.The contribution of this paper to the management accounting literature is twofold. First it suggests a method to improve causal maps by making visible two aspects, time and interconnections, that traditionally overlooked (Marr et al., 2004 Nørreklit, 2000). Second, it systematizes and extends the literature on how to account for time and on the relative importance of the temporal dimension in developing effective strategies.The main findings of the research can be summed up as follows. From a methodological point of view, the use of multi-method approach in designing a casual map offers the opportunity to take into considerations the intensity of the relations and the time lag between activities and effects. Adopting an IC perspective, it emerges that the properties of IC (i.e. connectivity, dynamicity and specificity) have a great impact on the map and the adoption of an IC perspective in designing casual maps may be useful to enrich this management tool and to better consider the specific properties of IC.

Making Great Theories

Making Great Theories

The Policy Effects of Legal and Institutional Reforms in Rural Areas in Response to Climate Change

This study was performed to predict the policy effects of the Fundamental Law on Climate Change (the legal and institutional reforms) on climate change that could adversely affect the agricultural sector. Since the climate change policy is difficult to determine its impacts compared to other large-scale government budget commitments, we selected the agricultural sector to simulate the impacts of the reforms on climate change. The results derived from the causal feedback map and simulation analysis are as follows. First, if the intensity is low, the policy could produce a problem in both agricultural productivity and local livelihoods. Second, a high degree of legal policy could not only increase agricultural productivity but also promote the economic conditions of the farmers, which could eventually contribute to local economic conditions. In addition, it promotes the economic conditions of the farmers. Therefore, the climate change policy was confirmed that can contribute to local economic.

Modelling multicriteria value interactions with Reasoning Maps

Idiographic causal maps are extensively employed in Operational Research to support problem structuring and complex decision making processes. They model means-end or causal discourses as a network of concepts connected by links denoting influence, thus enabling the representation of chains of arguments made by decision-makers. There have been proposals to employ such structures to support the structuring of multicriteria evaluation models, within an additive value measurement framework. However, a drawback of this multi-methodological modelling is the loss of richness of interactions along the means-end chains when evaluating options. This has led to the development of methods that make use of the structure of the map itself to evaluate options, such as the Reasoning Maps method, which employs ordinal scales and ordinal operators for such evaluation. However, despite their potential, Reasoning Maps cannot model explicitly value interactions nor perform a quantitative ranking of options, limiting their applicability and usefulness. In this article we propose extending the Reasoning Maps approach through a multilinear evaluation model structure, built with the MACBETH multicriteria method. The model explicitly captures the value interactions between concepts along the map and employs the MACBETH protocol of questioning to assess the strength of influence for each means-end link. The feasibility of the proposed approach to evaluate options and to deal with multicriteria interactions is tested in a real-world application to support the construction of a population health index.

A Creativity Support System Based on Causal Mapping

ABSTRACTTheory development is a very complex process that requires creativity and highly specialized analytical skills. This article presents a new algorithm, based on causal mapping, for assisting in the creation of qualitative theories. This algorithm is able to conjecture and prove new theorems, to test for consistency and completeness of the theory, and to derive meta-theorems comparing the different concepts in it. The use of the algorithm is exemplified in developing a theory to explain structural inertia in organizations.

Development of Effective Connectivity during Own- and Other-Race Face Processing: A Granger Causality Analysis.

Numerous developmental studies have suggested that other-race effect (ORE) in face recognition emerges as early as in infancy and develops steadily throughout childhood. However, there is very limited research on the neural mechanisms underlying this developmental ORE. The present study used Granger causality analysis (GCA) to examine the development of children's cortical networks in processing own- and other-race faces. Children were between 3 and 13 years. An old-new paradigm was used to assess their own- and other-race face recognition with ETG-4000 (Hitachi Medical Co., Japan) acquiring functional near infrared spectroscopy (fNIRS) data. After preprocessing, for each participant and under each face condition, we obtained the causal map by calculating the weights of causal relations between the time courses of [oxy-Hb] of each pair of channels using GCA. To investigate further the differential causal connectivity for own-race faces and other-race faces at the group level, a repeated measure analysis of variance (ANOVA) was performed on the GCA weights for each pair of channels with the face race task (own-race face vs. other-race face) as the within-subject variable and the age as a between-subject factor (continuous variable). We found an age-related increase in functional connectivity, paralleling a similar age-related improvement in behavioral face processing ability. More importantly, we found that the significant differences in neural functional connectivity between the recognition of own-race faces and that of other-race faces were modulated by age. Thus, like the behavioral ORE, the neural ORE emerges early and undergoes a protracted developmental course.

Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data

Prediction of seismic liquefaction is difficult due to the uncertainties and complexity of multiple related factors. Bayesian network is a just right effective tool to deal the problem because of merging multiple source information and domain knowledge in a consistent system, reflecting and analyzing the interdependent uncertain relationships between variables. This paper used two ways to construct generic Bayesian network models with twelve significant factors of seismic liquefaction, of which the first model is constructed only by interpretive structural modeling and causal mapping approach for incomplete data contained huge missing values. Another one is constructed by combining K2 algorithm and domain knowledge for complete data. Compared with artificial neural network and support vector machine using 5-fold cross-validation, the two Bayesian network models provided a better performance, and the second Bayesian network model is slightly better than the first one. This paper also offers a sensitivity analysis of the input factors. In the twelve variables, standard penetration test number, soil type, vertical effective stress, depth of soil deposit, and peak ground acceleration have more significant influences on seismic liquefaction than others. Our results suggest that the Bayesian network is useful for prediction of seismic liquefaction and is simple to perform in practice.

Using Textual Data in System Dynamics Model Conceptualization

Qualitative data is an important source of information for system dynamics modeling. It can potentially support any stage of the modeling process, yet it is mainly used in the early steps such as problem identification and model conceptualization. Existing approaches that outline a systematic use of qualitative data in model conceptualization are often not adopted for reasons of time constraints resulting from an abundance of data. In this paper, we introduce an approach that synthesizes the strengths of existing methods. This alternative approach (i) is focused on causal relationships starting from the initial steps of coding; (ii) generates a generalized and simplified causal map without recording individual relationships so that time consumption can be reduced; and (iii) maintains the links from the final causal map to the data sources by using software. We demonstrate an application of this approach in a study about integrated decision making in the housing sector of the UK.

Contribution of a Non-classical HLA Gene, HLA-DOA, to the Risk of Rheumatoid Arthritis

Despite the progress in human leukocyte antigen (HLA) causal variant mapping, independent localization of major histocompatibility complex (MHC) risk from classical HLA genes is challenging. Here, we conducted a large-scale MHC fine-mapping analysis of rheumatoid arthritis (RA) in a Japanese population (6,244 RA cases and 23,731 controls) population by using HLA imputation, followed by a multi-ethnic validation study including east Asian and European populations (n = 7,097 and 23,149, respectively). Our study identified an independent risk of a synonymous mutation at HLA-DOA, a non-classical HLA gene, on anti-citrullinated protein autoantibody (ACPA)-positive RA risk (p = 1.4× 10(-9)), which demonstrated a cis-expression quantitative trait loci (cis-eQTL) effect on HLA-DOA expression. Trans-ethnic comparison revealed different linkage disequilibrium (LD) patterns in HLA-DOA and HLA-DRB1, explaining the observed HLA-DOA variant risk heterogeneity among ethnicities, which was most evident in the Japanese population. Although previous HLA fine-mapping studies have identified amino acid polymorphisms of the classical HLA genes as driving genetic susceptibility to disease, our study additionally identifies the dosage contribution of a non-classical HLA gene to disease etiology. Our study contributes to theunderstanding of HLA immunology in human diseases and suggests the value of incorporating additional ancestry in MHC fine-mapping.