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Journal articles on the topic 'Diagramme Causaux'

Author: Grafiati

Published: 7 July 2024

Last updated: 7 July 2024

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1

Nicholls, Jim, and J. Kelly Russell. "Igneous Rock Associations 20. Pearce Element Ratio Diagrams: Linking Geochemical Data to Magmatic Processes." Geoscience Canada 43, no. 2 (2016): 133. http://dx.doi.org/10.12789/geocanj.2016.43.095.

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It has been nearly fifty years since Tom Pearce devised a type of element ratio diagram that isolates the effects of crystal fractionation and accumulation (sorting) hidden in the chemistry of a suite of igneous rocks. Here, we review the guiding principles and methods supporting the Pearce element ratio paradigm and provide worked examples with data from the Mauna Ulu lava flows (erupted 1970–1971, Kilauea Volcano, Hawaii). Construction of Pearce element ratio diagrams requires minimum data; a single rock analysis can suffice. The remaining data test the model. If the data fit the model, then

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2

Tipán, Luis, and Juan Carlos Muela. "Simulación causal para el consumo eléctrico residencial." Revista Técnica "energía" 17, no. 1 (2020): 60–70. http://dx.doi.org/10.37116/revistaenergia.v17.n1.2020.384.

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Un modelo dinámico basado en diagramas causales pretende identificar todos los factores involucrados en el comportamiento de un fenómeno especifico. El presente artículo implementa un modelo dinámico basado en diagramas causales que busca identificar el comportamiento y respectivo consumo eléctrico residencial. La metodología aplicada involucra variables aleatorias que buscan replicar el comportamiento estocástico al interior de una vivienda durante el día, la intención de aplicar un modelo causal radica en la interacción y dependencia existente entre variables, es decir el condicionamiento qu

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3

Schisterman, Enrique F., Neil J. Perkins, Sunni L. Mumford, Katherine A. Ahrens, and Emily M. Mitchell. "Collinearity and Causal Diagrams." Epidemiology 28, no. 1 (2017): 47–53. http://dx.doi.org/10.1097/ede.0000000000000554.

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4

Ogburn, Elizabeth L., and Tyler J. VanderWeele. "Causal Diagrams for Interference." Statistical Science 29, no. 4 (2014): 559–78. http://dx.doi.org/10.1214/14-sts501.

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5

Suzuki, Etsuji, Tomohiro Shinozaki, and Eiji Yamamoto. "Causal Diagrams: Pitfalls and Tips." Journal of Epidemiology 30, no. 4 (2020): 153–62. http://dx.doi.org/10.2188/jea.je20190192.

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6

Picciotto*, Sally. "Causal Diagrams and Their Uses." ISEE Conference Abstracts 2014, no. 1 (2014): 2901. http://dx.doi.org/10.1289/isee.2014.s-063.

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7

Mansournia, Mohammad A., Miguel A. Hernán, and Sander Greenland. "Matched designs and causal diagrams." International Journal of Epidemiology 42, no. 3 (2013): 860–69. http://dx.doi.org/10.1093/ije/dyt083.

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8

Greenland, Sander, Judea Pearl, and James M. Robins. "Causal Diagrams for Epidemiologic Research." Epidemiology 10, no. 1 (1999): 37–48. http://dx.doi.org/10.1097/00001648-199901000-00008.

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9

PEARL, JUDEA. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 669–88. http://dx.doi.org/10.1093/biomet/82.4.669.

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10

COX, D. R., and NANNY WERMUTH. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 688–89. http://dx.doi.org/10.1093/biomet/82.4.688.

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11

DAWID, A. P. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 689–90. http://dx.doi.org/10.1093/biomet/82.4.689.

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12

FIENBERG, STEPHEN E., CLARK GLYMOUR, and PETER SPIRTES. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 690–92. http://dx.doi.org/10.1093/biomet/82.4.690.

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13

FREEDMAN, DAVID. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 692–93. http://dx.doi.org/10.1093/biomet/82.4.692.

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14

IMBENS, GUIDO W., and DONALD B. RUBIN. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 694–95. http://dx.doi.org/10.1093/biomet/82.4.694.

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15

ROBINS, JAMES M. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 695–98. http://dx.doi.org/10.1093/biomet/82.4.695.

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16

ROSENBAUM, PAUL R. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 698–99. http://dx.doi.org/10.1093/biomet/82.4.698.

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17

SHAFER, GLENN. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 699–700. http://dx.doi.org/10.1093/biomet/82.4.699.

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18

SOBEL, MICHAEL E. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 700–702. http://dx.doi.org/10.1093/biomet/82.4.700.

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19

PEARL, JUDEA. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 702–10. http://dx.doi.org/10.1093/biomet/82.4.702.

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20

Shahar, Eyal, and Doron J. Shahar. "Causal diagrams and change variables." Journal of Evaluation in Clinical Practice 18, no. 1 (2010): 143–48. http://dx.doi.org/10.1111/j.1365-2753.2010.01540.x.

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21

Richardson, George P. "Problems with causal-loop diagrams." System Dynamics Review 2, no. 2 (1986): 158–70. http://dx.doi.org/10.1002/sdr.4260020207.

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22

Castello, Jonathan, Patrick Redmond, and Lindsey Kuper. "Inductive Diagrams for Causal Reasoning." Proceedings of the ACM on Programming Languages 8, OOPSLA1 (2024): 529–54. http://dx.doi.org/10.1145/3649830.

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The Lamport diagram is a pervasive and intuitive tool for informal reasoning about “happens-before” relationships in a concurrent system. However, traditional axiomatic formalizations of Lamport diagrams can be painful to work with in a mechanized setting like Agda. We propose an alternative, inductive formalization — the causal separation diagram (CSD) — that takes inspiration from string diagrams and concurrent separation logic, but enjoys a graphical syntax similar to Lamport diagrams. Critically, CSDs are based on the idea that causal relationships between events are witnessed by the paths

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23

Sousa Rodrigues Duarte, Ingrid de, and Antony Marco Mota Polito. "Uma abordagem ausubeliana para o ensino do eletromagnetismo na interpretação causal." Revista de Enseñanza de la Física 35, no. 2 (2023): 243–57. http://dx.doi.org/10.55767/2451.6007.v35.n2.43737.

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Apresentamos as principais ideias associadas com uma proposta de abordagem ausubeliana para o ensino integrado da fenomenologia e das leis do eletromagnetismo, enfatizando sua interpretação causal. Desenvolve-se, progressivamente, os conceitos de eletrostática, de magnetostática, de eletrodinâmica, de campo eletromagnético e de ondas eletromagnéticas, assumindo o papel central do conceito de causalidade. Por hipótese, o princípio de causalidade deve funcionar como uma chave ausubeliana que, atuando como parte de (potenciais) estruturas subsunçoras, supomos ser, nesse contexto, efetiva para alc

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24

Li, Ang, and Judea Pearl. "Unit Selection with Causal Diagram." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5765–72. http://dx.doi.org/10.1609/aaai.v36i5.20519.

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The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged. Using a combination of experimental and observational data, Li and Pearl derived tight bounds on the "benefit function" - the payoff/cost associated with selecting an individual with given characteristics. This paper shows that these bounds can be narrowed significantly (enough to change decisions) when structural information is available in the form of a caus

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25

Sánchez-Niubò, Albert, Carlos G. Forero, and Fernando G. Benavides. "The application of causal diagrams to conceptualize mechanisms in occupational epidemiology." Archivos de Prevención de Riesgos Laborales 19, no. 2 (2016): 103–6. http://dx.doi.org/10.12961/aprl.2016.19.02.4.

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26

HU Xiaoxuan, JIANG Fan, and XIA Wei. "Causal Influence Diagrams for Decision-making." Journal of Convergence Information Technology 8, no. 5 (2013): 397–407. http://dx.doi.org/10.4156/jcit.vol8.issue5.46.

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27

Oates, Chris J., Jessica Kasza, Julie A. Simpson, and Andrew B. Forbes. "Repair of Partly Misspecified Causal Diagrams." Epidemiology 28, no. 4 (2017): 548–52. http://dx.doi.org/10.1097/ede.0000000000000659.

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28

Richardson, George P. "Problems in causal loop diagrams revisited." System Dynamics Review 13, no. 3 (1997): 247–52. http://dx.doi.org/10.1002/(sici)1099-1727(199723)13:3<247::aid-sdr128>3.0.co;2-9.

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29

Lutomski, Jennifer E., A. Rogier T. Donders, and René J. F. Melis. "Causal Diagrams to Better Understand Missingness." JAMA Pediatrics 168, no. 2 (2014): 187. http://dx.doi.org/10.1001/jamapediatrics.2013.3650.

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30

Roatta, Analía, and Reinaldo Welti. "Efecto Doppler para pulsos y su representación en el plano (x, t)." Revista Brasileira de Ensino de Física 31, no. 1 (2009): 1304.1–1304.7. http://dx.doi.org/10.1590/s1806-11172009000100004.

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Los textos de física de nivel universitario generalmente discuten el efecto Doppler como un tema vinculado sólo a las ondas armónicas, sin embargo, en términos más amplios el efecto está vinculado a compresiones y expansiones, en el dominio del espacio y del tiempo, de pulsos de cualquier tipo. En este trabajo, se estudia analíticamente el efecto que introduce la fuente, o el observador, en movimiento sobre la extensión espacial y temporal del pulso. Se utiliza también diagramas en el plano (x, t), para analizar las compresiones (o expansiones) tanto temporales como espaciales del pulso causad

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31

Jacobs, Bart, Aleks Kissinger, and Fabio Zanasi. "Causal inference via string diagram surgery." Mathematical Structures in Computer Science 31, no. 5 (2021): 553–74. http://dx.doi.org/10.1017/s096012952100027x.

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Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent

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32

Silva, Samuel Bloch, Marcelo Dias Ferreira, Mischel Carmen Neyra Belderrain, and Anderson Ribeiro Correia. "Modelagem da Cadeia Logística de Reposição de Componentes Aeronáuticos no contexto do System Dynamics." Aplicações Operacionais em Áreas de Defesa 22 (September 30, 2021): 19–24. http://dx.doi.org/10.55972/spectrum.v22i1.322.

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Desde a década de 60, a metodologia conhecida como Dinâmica de Sistemas (System Dynamics - SD) vem se destacando como ferramenta para analisar a dinâmica dos sistemas produtivos e logísticos. Através de diversos tipos de diagramas (causais, estoque e fluxo) é possível expressar graficamente um sistema, possibilitando representar a complexidade dinâmica das relações entre as partes ao longo do tempo. Isto envolve desde a análise estrutural até a definição de políticas de tomada de decisão um determinado ambiente produtivo. O presente trabalho apresenta resultados relativos ao apoio à tomada de

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33

Dawid, A. P. "Influence Diagrams for Causal Modelling and Inference." International Statistical Review / Revue Internationale de Statistique 70, no. 2 (2002): 161. http://dx.doi.org/10.2307/1403901.

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34

Witmer, Jeff. "Simpson’s Paradox, Visual Displays, and Causal Diagrams." American Mathematical Monthly 128, no. 7 (2021): 598–610. http://dx.doi.org/10.1080/00029890.2021.1932237.

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35

Williamson, Elizabeth J., Zoe Aitken, Jock Lawrie, Shyamali C. Dharmage, John A. Burgess, and Andrew B. Forbes. "Introduction to causal diagrams for confounder selection." Respirology 19, no. 3 (2014): 303–11. http://dx.doi.org/10.1111/resp.12238.

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36

Hernan, M. A., and S. R. Cole. "Invited Commentary: Causal Diagrams and Measurement Bias." American Journal of Epidemiology 170, no. 8 (2009): 959–62. http://dx.doi.org/10.1093/aje/kwp293.

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37

Dawid, A. P. "Influence Diagrams for Causal Modelling and Inference." International Statistical Review 70, no. 2 (2002): 161–89. http://dx.doi.org/10.1111/j.1751-5823.2002.tb00354.x.

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38

Aste, Andreas. "Two-Loop Diagrams in Causal Perturbation Theory." Annals of Physics 257, no. 2 (1997): 158–204. http://dx.doi.org/10.1006/aphy.1997.5686.

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39

Crabolu, Gloria, Xavier Font, and Sibel Eker. "Evaluating policy complexity with Causal Loop Diagrams." Annals of Tourism Research 100 (May 2023): 103572. http://dx.doi.org/10.1016/j.annals.2023.103572.

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40

Naser, M. Z. "Causal diagrams for civil and structural engineers." Structures 63 (May 2024): 106398. http://dx.doi.org/10.1016/j.istruc.2024.106398.

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41

Aste, Andreas. "Causal Construction of the Massless Vertex Diagram." Letters in Mathematical Physics 78, no. 2 (2006): 157–72. http://dx.doi.org/10.1007/s11005-006-0113-3.

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42

De Julio, Marcelo, Eduardo Fausto de Almeida Neves, Julio Cesar Trofino, and Luiz Di Bernardo. "Emprego do reagente de fenton como agente coagulante na remoção de substâncias húmicas de água por meio da flotação por ar dissolvido e filtração." Engenharia Sanitaria e Ambiental 11, no. 3 (2006): 260–68. http://dx.doi.org/10.1590/s1413-41522006000300009.

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O reagente de Fenton foi empregado como agente coagulante no tratamento de água com cor verdadeira elevada (100 ± 5 uH) causada pela introdução de substâncias húmicas extraídas de turfa, empregando-se a flotação por ar dissolvido. Otimizou-se o par de valores dosagem de coagulante x pH de coagulação para posterior construção dos diagramas de coagulação, obtendo-se eficiências de remoção de cor aparente pouco superiores a 60%. Procurou-se simular um tratamento em ciclo completo, realizando-se ensaio de filtração em areia após flotação, obtendo-se efluente de excelente qualidade, apresentando co

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43

Tinoco, Maria Auxiliadora Cannarozzo, and José Luis Duarte Ribeiro. "Uma nova abordagem para a modelagem das relações entre os determinantes da satisfação dos clientes de serviços." Production 17, no. 3 (2007): 454–70. http://dx.doi.org/10.1590/s0103-65132007000300005.

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Este artigo apresenta uma nova abordagem para a modelagem das relações entre os determinantes da satisfação dos clientes de serviços. As principais vantagens da abordagem proposta em relação aos métodos tradicionais de modelagem, como, por exemplo, as equações estruturais, são: (i) possibilidade de identificar as relações entre múltiplos determinantes utilizando um tamanho de amostra relativamente pequeno, (ii) utilização de um procedimento estatístico mais simples, (iii) possibilidade de minimizar o erro de especificação no processo de modelagem, visto que podem ser consideradas todas as vari

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44

Hamra, Ghassan, Jay Kaufman, and Anjel Vahratian. "Model Averaging for Improving Inference from Causal Diagrams." International Journal of Environmental Research and Public Health 12, no. 8 (2015): 9391–407. http://dx.doi.org/10.3390/ijerph120809391.

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45

Jupiter, Daniel C. "Causal Diagrams and Multivariate Analysis II: Precision Work." Journal of Foot and Ankle Surgery 53, no. 6 (2014): 829–31. http://dx.doi.org/10.1053/j.jfas.2014.08.023.

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46

Jupiter, Daniel C. "Causal Diagrams and Multivariate Analysis III: Confound It!" Journal of Foot and Ankle Surgery 54, no. 1 (2015): 145–47. http://dx.doi.org/10.1053/j.jfas.2014.11.003.

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47

Gaskell, Amy L., and Jamie W. Sleigh. "An Introduction to Causal Diagrams for Anesthesiology Research." Anesthesiology 132, no. 5 (2020): 951–67. http://dx.doi.org/10.1097/aln.0000000000003193.

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Abstract Making good decisions in the era of Big Data requires a sophisticated approach to causality. We are acutely aware that association ≠ causation, yet untangling the two remains one of our greatest challenges. This realization has stimulated a Causal Revolution in epidemiology, and the lessons learned are highly relevant to anesthesia research. This article introduces readers to directed acyclic graphs; a cornerstone of modern causal inference techniques. These diagrams provide a robust framework to address sources of bias and discover causal effects. We use the topical question of wheth

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48

Shahar, E. "Shahar Responds to "Causal Diagrams and Measurement Bias"." American Journal of Epidemiology 170, no. 8 (2009): 963–64. http://dx.doi.org/10.1093/aje/kwp289.

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49

Scholl, Raphael. "Spot the difference: Causal contrasts in scientific diagrams." Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 60 (December 2016): 77–87. http://dx.doi.org/10.1016/j.shpsc.2016.06.003.

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50

Pala, Özge, Dirk J. Vriens, and Jac AM Vennix. "Causal loop diagrams as a de-escalation technique." Journal of the Operational Research Society 66, no. 4 (2015): 593–601. http://dx.doi.org/10.1057/jors.2014.24.

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