Literatura académica sobre el tema "Diagramme Causaux"
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Artículos de revistas sobre el tema "Diagramme Causaux"
Nicholls, Jim y J. Kelly Russell. "Igneous Rock Associations 20. Pearce Element Ratio Diagrams: Linking Geochemical Data to Magmatic Processes". Geoscience Canada 43, n.º 2 (18 de mayo de 2016): 133. http://dx.doi.org/10.12789/geocanj.2016.43.095.
Texto completoTipán, Luis y Juan Carlos Muela. "Simulación causal para el consumo eléctrico residencial". Revista Técnica "energía" 17, n.º 1 (30 de julio de 2020): 60–70. http://dx.doi.org/10.37116/revistaenergia.v17.n1.2020.384.
Texto completoSchisterman, Enrique F., Neil J. Perkins, Sunni L. Mumford, Katherine A. Ahrens y Emily M. Mitchell. "Collinearity and Causal Diagrams". Epidemiology 28, n.º 1 (enero de 2017): 47–53. http://dx.doi.org/10.1097/ede.0000000000000554.
Texto completoOgburn, Elizabeth L. y Tyler J. VanderWeele. "Causal Diagrams for Interference". Statistical Science 29, n.º 4 (noviembre de 2014): 559–78. http://dx.doi.org/10.1214/14-sts501.
Texto completoSuzuki, Etsuji, Tomohiro Shinozaki y Eiji Yamamoto. "Causal Diagrams: Pitfalls and Tips". Journal of Epidemiology 30, n.º 4 (5 de abril de 2020): 153–62. http://dx.doi.org/10.2188/jea.je20190192.
Texto completoPicciotto*, Sally. "Causal Diagrams and Their Uses". ISEE Conference Abstracts 2014, n.º 1 (20 de octubre de 2014): 2901. http://dx.doi.org/10.1289/isee.2014.s-063.
Texto completoMansournia, Mohammad A., Miguel A. Hernán y Sander Greenland. "Matched designs and causal diagrams". International Journal of Epidemiology 42, n.º 3 (junio de 2013): 860–69. http://dx.doi.org/10.1093/ije/dyt083.
Texto completoGreenland, Sander, Judea Pearl y James M. Robins. "Causal Diagrams for Epidemiologic Research". Epidemiology 10, n.º 1 (enero de 1999): 37–48. http://dx.doi.org/10.1097/00001648-199901000-00008.
Texto completoPEARL, JUDEA. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 669–88. http://dx.doi.org/10.1093/biomet/82.4.669.
Texto completoCOX, D. R. y NANNY WERMUTH. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 688–89. http://dx.doi.org/10.1093/biomet/82.4.688.
Texto completoTesis sobre el tema "Diagramme Causaux"
Pressat-Laffouilhère, Thibaut. "Modèle ontologique formel, un appui à la sélection des variables pour la construction des modèles multivariés". Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMR104.
Texto completoResponding to a causal research question in the context of observational studies requires the selection ofconfounding variables. Integrating them into a multivariate model as co-variables helps reduce bias in estimatingthe true causal effect of exposure on the outcome. Identification is achieved through causal diagrams (CDs) ordirected acyclic graphs (DAGs). These representations, composed of nodes and directed arcs, prevent theselection of variables that would introduce bias, such as mediating and colliding variables. However, existingmethods for constructing CDs lack systematic approaches and exhibit limitations in terms of formalism,expressiveness, and completeness. To offer a formal and comprehensive framework capable of representing allnecessary information for variable selection on an enriched CD, analyzing this CD, and, most importantly,explaining the analysis results, we propose utilizing an ontological model enriched with inference rules. Anontological model allows for representing knowledge in the form of an expressive and formal graph consisting ofclasses and relations similar to the nodes and arcs of Cds. We developed the OntoBioStat (OBS) ontology basedon a list of competency questions about variable selection and an analysis of scientific literature on CDs andontologies. The construction framework of OBS is richer than that of a CD, incorporating implicit elements likenecessary causes, study context, uncertainty in knowledge, and data quality. To evaluate the contribution of OBS,we used it to represent variables from a published observational study and compared its conclusions with thoseof a CD. OBS identified new confounding variables due to its different construction framework and the axiomsand inference rules. OBS was also used to represent an ongoing retrospective study analysis. The modelexplained statistical correlations found between study variables and highlighted potential confounding variablesand their possible substitutes (proxies). Information on data quality and causal relation uncertainty facilitatedproposing sensitivity analyses, enhancing the study's conclusion robustness. Finally, inferences were explainedthrough the reasoning capabilities provided by OBS's formal representation. Ultimately, OBS will be integratedinto statistical analysis tools to leverage existing libraries for variable selection, making it accessible toepidemiologists and biostatisticians
Cortes, Taísa Rodrigues. "Utilização de diagramas causais em confundimento e viés de seleção". Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8442.
Texto completoApesar do crescente reconhecimento do potencial dos diagramas causais por epidemiologistas, essa técnica ainda é pouco utilizada na investigação epidemiológica. Uma das possíveis razões é que muitos temas de investigação exigem modelos causais complexos. Neste trabalho, a relação entre estresse ocupacional e obesidade é utilizada como um exemplo de aplicação de diagramas causais em questões relacionadas a confundimento. São apresentadas etapas da utilização dos diagramas causais, incluindo a construção do gráfico acíclico direcionado, seleção de variáveis para ajuste estatístico e a derivação das implicações estatísticas de um diagrama causal. A principal vantagem dos diagramas causais é tornar explícitas as hipóteses adjacentes ao modelo considerado, permitindo que suas implicações possam ser analisadas criticamente, facilitando, desta forma, a identificação de possíveis fontes de viés e incerteza nos resultados de um estudo epidemiológico.
Despite the increasing recognition of the potential of causal diagrams by epidemiologists, this technique has not been widely used in epidemiological research. One possible reason is that many research topics require complex causal models. In this article, the relationship between occupational stress and obesity is used as an example of application of causal diagrams on confounding. Some steps are presented, including the construction of the directed acyclic graph, the selection of variables for statistical control and the derivation of the statistical implications of a causal diagram. The main advantage of causal diagrams is to make the assumptions explicit, thus facilitating critical evaluations and the identification of possible sources of bias and uncertainty in the results of an epidemiological study.
Madry, Martin. "Systémová dynamika: případ výkonnosti projektových týmů". Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-193285.
Texto completoArévalo, Mejía Julia Elvira y Alania Macario charles Sobero. "“Incumplimiento con la calidad adecuada en los procesos constructivos de obras de edificación”, caso de estudio de centro comercial". Master's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/653704.
Texto completoThis work focuses on quality improvement concerning the structural elements of a shopping center, in order to reduce and minimize the most relevant Non-Conformities that occurred on site. The project was based on the construction and expansion of tenants of a shopping center that will be rented as its purpose. By applying the Root Cause Analysis and using the Ishikawa diagram and Pareto diagram tools, it was possible to find the possible causes of quality noncompliance in the structural elements, which were subsequently validated in order to determine corrective actions. In the first chapter the problem statement, main and secondary problems, justification for the study, limitation and general and specific objectives are indicated. In the Second Chapter the theoretical framework is pointed out, where it mentions the quality in Peru, the total quality management, the costs of quality in construction, quality engineering and definitions. The third chapter indicates the use of Root Cause Analysis, the Cause Effect Diagram and Pareto Diagram tools. In the fourth chapter, the development of root cause analysis is presented using a sequence of steps. In the fifth chapter, The Economic Evaluation, Construction Budget, Repair Cost and Incurred Expense Analysis. Finally, in chapter six, the conclusions and recommendations of this work will be presented.
Trabajo de investigación
Laurenti, Rafael. "The Karma of Products : Exploring the Causality of Environmental Pressure with Causal Loop Diagram and Environmental Footprint". Doctoral thesis, KTH, Industriell ekologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-184223.
Texto completoJury committee
Henrikke Baumann, Associate Professor
Chalmers University of Technology
Department of Energy and Environment
Division of Environmental System Analysis
Joakim Krook, Associate Professor
Linköpings Universitet
Department of Management and Engineering (IEI) / Environmental Technology and Management (MILJÖ)
Karl Johan Bonnedal, Associate Professor
Umeå University
Umeå School of Business and Economics (USBE)
Sofia Ritzén, Professor
KTH Royal Institute of Technology
School of Industrial Engineering and Management
Department of Machine Design
Integrated Product Development
QC 20160405
Ziebart, Brian D. "Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy". Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/17.
Texto completoStröm, Simon. "Samrådsunderlag för Lysekilsprojektet : Forskning och utveckling av vågkraft". Thesis, Stockholms universitet, Institutionen för naturgeografi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-114303.
Texto completoLysekilsprojektet
OLIVEIRA, Felipe Andrade Gama de. "Avaliação probabilística de risco via modelo causal híbrido em cirurgia: o caso da histerectomia vaginal". Universidade Federal de Pernambuco, 2006. https://repositorio.ufpe.br/handle/123456789/5851.
Texto completoA análise probabilística de risco é uma metodologia que identifica, avalia e quantifica os riscos nos mais diversos procedimentos, desde de sistemas de alta complexidade tecnológica a sistemas onde só existe o homem executando tarefas. Esta análise tem como objetivo melhorar a segurança e o desempenho destes processos. A área de saúde ainda encontra-se bastante carente de estudos que analisem e quantifiquem os riscos envolvidos nos seus procedimentos. E é com este intuito, que este trabalho propõe uma metodologia de avaliação probabilística de risco para cirurgias, sendo apresentado o caso da histerectomia vaginal. Esta análise aborda tanto os aspectos da confiabilidade humana como a confiabilidade dos equipamentos utilizados. No modelo híbrido proposto, a análise de riscos é baseada na integração dos diagramas de seqüências de eventos, árvore de falhas e redes Bayesianas. Na modelagem os eventos pivotais dos diagramas de seqüência de eventos relacionados a erros humanos, ou seja, resultantes diretamente de ações humanas, são modelados via redes Bayesianas, proporcionando uma representação mais realista da natureza dinâmica destas ações, enquanto que os eventos pivotais relacionados à falha de equipamentos são modelados via árvores de falhas. Assim esta metodologia contribui para a melhoria do processo de gerenciamento dos riscos envolvidos durante a execução da atividade cirúrgica
Arias, Trujillo Milagros. "Aplicación del diagrama causa-efecto para identificar los principales riesgos ante un posible siniestro en el planeamiento de una auditoría de procesos". Bachelor's thesis, Universidad Nacional Mayor de San Marcos, 2008. https://hdl.handle.net/20.500.12672/12652.
Texto completoTrabajo de suficiencia profesional
Rawlins, Jonathan Mark. "Exploring the suitability of causal loop diagrams to assess the value chains of aquatic ecosystem services: a case study of the Baviaanskloof, South Africa". Thesis, Rhodes University, 2017. http://hdl.handle.net/10962/4909.
Texto completoLibros sobre el tema "Diagramme Causaux"
Kern, Johannes. Utilizar con éxito Los Diagramas de Causa-Efecto: El Diagrama de Ishikawa en la Teoría y la Práctica. Independently Published, 2021.
Buscar texto completoCoecke, Bob y Aleks Kissinger. Categorical Quantum Mechanics I: Causal Quantum Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198748991.003.0012.
Texto completoWittman, David M. Time Skew. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199658633.003.0006.
Texto completoHoyle, Rick H. Applications of structural equation modelling in clinical and health psychology research. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0020.
Texto completoGarcia, Juan Martin. Feedbacks. from Causal Diagrams to System Thinking: Manage Dynamical Systems in Business, Econony, Biology and Social Sciences, Using Balancing and Reinforcing Loops. Independently Published, 2018.
Buscar texto completoZaanen, Jan. On Time. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/9780198920793.001.0001.
Texto completoCapítulos de libros sobre el tema "Diagramme Causaux"
Turner, J. Rick. "Causal Diagrams". En Encyclopedia of Behavioral Medicine, 360–61. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_993.
Texto completoTurner, J. Rick. "Causal Diagrams". En Encyclopedia of Behavioral Medicine, 401–2. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_993.
Texto completoGreenland, Sander y Judea Pearl. "Causal Diagrams". En International Encyclopedia of Statistical Science, 208–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_162.
Texto completoHuntington-Klein, Nick. "Causal Diagrams". En The Effect, 87–100. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003226055-7.
Texto completoGudlaugsson, Bjarnhedinn, Huda Dawood, Gobind Pillai y Michael Short. "First Step Towards a System Dynamic Sustainability Assessment Model for Urban Energy Transition". En Springer Proceedings in Energy, 225–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_28.
Texto completoInghels, Dirk. "Causal Loop Diagrams". En Introduction to Modeling Sustainable Development in Business Processes, 149–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58422-1_8.
Texto completoBala, Bilash Kanti, Fatimah Mohamed Arshad y Kusairi Mohd Noh. "Causal Loop Diagrams". En Springer Texts in Business and Economics, 37–51. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2045-2_3.
Texto completoBarbrook-Johnson, Pete y Alexandra S. Penn. "Causal Loop Diagrams". En Systems Mapping, 47–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01919-7_4.
Texto completoSherwood, Dennis. "Causal Loop Diagrams". En Strategic Thinking Illustrated, 23–36. New York: Productivity Press, 2022. http://dx.doi.org/10.4324/9781003304050-4.
Texto completoHuntington-Klein, Nick. "Drawing Causal Diagrams". En The Effect, 101–14. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003226055-8.
Texto completoActas de conferencias sobre el tema "Diagramme Causaux"
Kalinowski, Marcos y Guilherme Horta Travassos. "Uma Abordagem Probabilística para Análise Causal de Defeitos de Software". En Simpósio Brasileiro de Qualidade de Software. Sociedade Brasileira de Computação - SBC, 2012. http://dx.doi.org/10.5753/sbqs.2012.15335.
Texto completoErwig, Martin y Eric Walkingshaw. "Causal Reasoning with Neuron Diagrams". En 2010 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2010. http://dx.doi.org/10.1109/vlhcc.2010.23.
Texto completoLübke, Karsten y Matthias Gehrke. "Causal Diagrams for Descriptive Statistics". En Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t3b1.
Texto completoMargetts, Rebecca y Roger F. Ngwompo. "Comparison of Modeling Techniques for a Landing Gear". En ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-39722.
Texto completoRios, Nicolli, Rodrigo Spínola y Manoel Mendonça. "Organização de um Conjunto de Descobertas Experimentais sobre Causas e Efeitos da Dívida Técnica através de uma Família de Surveys Globalmente Distribuída". En Anais Estendidos do Congresso Brasileiro de Software: Teoria e Prática. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/cbsoft_estendido.2021.17296.
Texto completoMurawiowa, Nelly, Elena Mudrova y Viktoria Degtereva. "Smart Housing and Utilities: A Causal Diagram". En SPBPU IDE-2021: 3rd International Scientific Conference on Innovations in Digital Economy. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3527049.3527134.
Texto completoFox, James, Tom Everitt, Ryan Carey, Eric Langlois, Alessandro Abate y Michael Wooldridge. "PyCID: A Python Library for Causal Influence Diagrams". En Python in Science Conference. SciPy, 2021. http://dx.doi.org/10.25080/majora-1b6fd038-008.
Texto completoNiu, Xueyan, Xiaoyun Li y Ping Li. "Learning Cluster Causal Diagrams: An Information-Theoretic Approach". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/675.
Texto completoZhang, Pan, Raúl Leal Ascencio y Giles Poulsom. "Exploring Mobile Banking Adoption through Causal-Loop Diagrams". En 4th European International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International, 2021. http://dx.doi.org/10.46254/eu04.20210174.
Texto completoSubramonyam, Hari, Eytan Adar, Priti Shah y Colleen M. Seifert. "Causal Pattern Diagrams in Science Texts Support Explanation". En 18th International Conference of the Learning Sciences (ICLS) 2024. International Society of the Learning Sciences, 2024. http://dx.doi.org/10.22318/icls2024.106268.
Texto completoInformes sobre el tema "Diagramme Causaux"
Blake, Carolyn, Benjamin P. Rigby, Roxanne Armstrong-Moore, Peter Barbrook-Johnson, Nigel Gilbert, Mohammad Hassannezhad, Petra Meier et al. Participatory systems mapping for population health research, policy and practice: guidance on method choice and design. University of Glasgow, enero de 2024. http://dx.doi.org/10.36399/gla.pubs.316563.
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