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Littérature scientifique sur le sujet « Causal graphs »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Causal graphs"

Atherton, Juli, Derek Ruths, and Adrian Vetta. "Computation in Causal Graphs." Journal of Graph Algorithms and Applications 23, no. 2 (2019): 317–44. http://dx.doi.org/10.7155/jgaa.00493.

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Lipsky, Ari M., and Sander Greenland. "Causal Directed Acyclic Graphs." JAMA 327, no. 11 (2022): 1083. http://dx.doi.org/10.1001/jama.2022.1816.

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Kischka, Peter, and Dietrich Eherler. "Causal graphs and unconfoundedness." Allgemeines Statistisches Archiv 85, no. 3 (2001): 247–66. http://dx.doi.org/10.1007/s101820100064.

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Kinney, David. "Curie’s principle and causal graphs." Studies in History and Philosophy of Science Part A 87 (June 2021): 22–27. http://dx.doi.org/10.1016/j.shpsa.2021.02.007.

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Gimenez, O., and A. Jonsson. "The Complexity of Planning Problems With Simple Causal Graphs." Journal of Artificial Intelligence Research 31 (February 26, 2008): 319–51. http://dx.doi.org/10.1613/jair.2432.

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We present three new complexity results for classes of planning problems with simple causal graphs. First, we describe a polynomial-time algorithm that uses macros to generate plans for the class 3S of planning problems with binary state variables and acyclic causal graphs. This implies that plan generation may be tractable even when a planning problem has an exponentially long minimal solution. We also prove that the problem of plan existence for planning problems with multi-valued variables and chain causal graphs is NP-hard. Finally, we show that plan existence for planning problems with bi

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Jonsson, Anders, Peter Jonsson, and Tomas Lööw. "When Acyclicity Is Not Enough: Limitations of the Causal Graph." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 117–25. http://dx.doi.org/10.1609/icaps.v23i1.13550.

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Causal graphs are widely used in planning to capture the internal structure of planning instances. In the past, causal graphs have been exploited to generate hierarchical plans, to compute heuristics, and to identify classes of planning instances that are easy to solve. It is generally believed that planning is easier when the causal graph is acyclic. In this paper we show that this is not true in the worst case, proving that the problem of plan existence is PSPACE-complete even when the causal graph is acyclic. Since the variables of the planning instances in our reduction are propositional,

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Puente, C., A. Sobrino, J. A. Olivas, and E. Garrido. "Summarizing information by means of causal sentences through causal graphs." Journal of Applied Logic 24 (November 2017): 3–14. http://dx.doi.org/10.1016/j.jal.2016.11.020.

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Wieczorek, Aleksander, and Volker Roth. "Information Theoretic Causal Effect Quantification." Entropy 21, no. 10 (2019): 975. http://dx.doi.org/10.3390/e21100975.

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Modelling causal relationships has become popular across various disciplines. Most common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) and the Neyman-Rubin potential outcome framework. In this paper, we propose an information theoretic framework for causal effect quantification. To this end, we formulate a two step causal deduction procedure in the Pearl and Rubin frameworks and introduce its equivalent which uses information theoretic terms only. The first step of the procedure consists of ensuring no confounding or finding an adjustment set with directed in

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Levine, Eli, and J. Butler. "Causal Graphs and Concept-Mapping Assumptions." Applied System Innovation 1, no. 3 (2018): 25. http://dx.doi.org/10.3390/asi1030025.

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Determining what constitutes a causal relationship between two or more concepts, and how to infer causation, are fundamental concepts in statistics and all the sciences. Causation becomes especially difficult in the social sciences where there is a myriad of different factors that are not always easily observed or measured that directly or indirectly influence the dynamic relationships between independent variables and dependent variables. This paper proposes a procedure for helping researchers explicitly understand what their underlying assumptions are, what kind of data and methodology are n

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Chindelevitch, Leonid, Po-Ru Loh, Ahmed Enayetallah, Bonnie Berger, and Daniel Ziemek. "Assessing statistical significance in causal graphs." BMC Bioinformatics 13, no. 1 (2012): 35. http://dx.doi.org/10.1186/1471-2105-13-35.

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Thèses sur le sujet "Causal graphs"

Simonne, Lucas. "Mining differential causal rules in knowledge graphs." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG008.

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La fouille de règles d'association au sein de graphes de connaissances est un domaine de recherche important.En effet, ce type de règle permet de représenter des connaissances, et leur application permet de compléter un graphe en ajoutant des données manquantes ou de supprimer des données erronées.Cependant, ces règles ne permettent pas d'exprimer des relations causales, dont la sémantique diffère d'une simple association ou corrélation. Dans un système, un lien de causalité entre une variable A et une variable B est une relation orientée de A vers B et indique qu'un changement dans A cause un

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Choudhry, Arjun. "Narrative Generation to Support Causal Exploration of Directed Graphs." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98670.

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Causal graphs are a useful notation to represent the interplay between the actors as well as the polarity and strength of the relationship that they share. They are used extensively in educational, professional, and industrial contexts to simulate different scenarios, validate behavioral aspects, visualize the connections between different processes, and explore the adversarial effects of changing certain nodes. However, as the size of the causal graphs increase, interpreting them also becomes increasingly tougher. In such cases, new analytical tools are required to enhance the user's comprehe

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Bernigau, Holger. "Causal Models over Infinite Graphs and their Application to the Sensorimotor Loop." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-164734.

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Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a s

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Yang, Karren Dai. "Learning causal graphs under interventions and applications to single-cell biological data analysis." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130806.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February, 2021<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021<br>Cataloged from the official PDF version of thesis.<br>Includes bibliographical references (pages 49-51).<br>This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using

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Cabeli, Vincent. "Learning causal graphs from continuous or mixed datasets of biological or clinical interest." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS331.

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Les travaux de cette thèse s’inscrivent dans la théorie principalement développée par Judea Pearl sur les diagrammes causaux; des modèles graphiques qui permettent de dériver toutes les quantités causales d’intérêt formellement et intuitivement. Nous traitons le problème de l’inférence de réseau causal à partir uniquement de données d’observation, c’est-à-dire sans aucune intervention de la part de l’expérimentateur. En particulier, nous proposons d’améliorer les méthodes existantes pour les rendre plus aptes à analyser des données issues du monde réel, en nous affranchissant le plus possible

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Giasemidis, Georgios. "Spectral dimension in graph models of causal quantum gravity." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:d0aaa6f2-dd0b-4ea9-81c1-7c9e81a7229e.

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The phenomenon of scale dependent spectral dimension has attracted special interest in the quantum gravity community over the last eight years. It was first observed in computer simulations of the causal dynamical triangulation (CDT) approach to quantum gravity and refers to the reduction of the spectral dimension from 4 at classical scales to 2 at short distances. Thereafter several authors confirmed a similar result from different approaches to quantum gravity. Despite the contribution from different approaches, no analytical model was proposed to explain the numerical results as the continu

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CALIGARIS, SILVIA. "A Causal Graphs - based approach for assessing gender disparities: an application to child health & nutrition in China." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/83241.

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Most of gender statistical measures proposed in the last decades are in fact composite indicators, i.e. weighted linear combinations of basic statistics such as ratios, percentages etc. Composite indicators then involves several arbitrary choices -for instance the weighting/aggregating system, variables selection, standardization affecting both indexes transparency and interpretation. Furthermore gender inequality is a complex latent phenomenon, a collection of disparate and inter-linked issues that can be hardly caught in a single indicator. The development of statistical tools and

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Bernigau, Holger [Verfasser], Nihat [Akademischer Betreuer] Ay, Nihat [Gutachter] Ay, and Shun-ichi [Gutachter] Amari. "Causal Models over Infinite Graphs and their Application to the Sensorimotor Loop : Causal Models over Infinite Graphs and their Application to theSensorimotor Loop / Holger Bernigau ; Gutachter: Nihat Ay, Shun-ichi Amari ; Betreuer: Nihat Ay." Leipzig : Universitätsbibliothek Leipzig, 2015. http://d-nb.info/1239565127/34.

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Chong, Hogun. "A causal model of linkages among strategy, structure, and performance using directed acyclic graphs: A manufacturing subset of Fortune 500 industrials 1990-1998." Texas A&M University, 2003. http://hdl.handle.net/1969.1/58.

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This research explored the causal relationships among strategies, corporate structure, and performance of the largest U.S. non-financial firms using Directed Acyclic Graphs (DAGs). Corporate strategies and structure have been analyzed as major variables to influence corporate performance in management and organizational studies. However, their causal relationships in terms of which variables are leaders and followers, as well as the choices of variables to configure them, are controversial. Finding of causal relationships among strategic variables, structural variables, and corporate performan

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Aka, Niels Mariano [Verfasser]. "Three Essays on Model Selection in Time Series Econometrics : Model Averaging, Causal Graphs, and Structural Identification / Niels Mariano Aka." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1229436685/34.

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Livres sur le sujet "Causal graphs"

Yao, Qing. Directed acyclic graphs, linear recursive regression, and inference about causal ordering. University of Toronto, Dept. of Statistics, 1993.

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Isakov, Vladimir. Speak the language of schemes. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1860649.

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Schematization and visualization are the necessary means to ensure the activity of a modern specialist. Schematization allows you to highlight the main thing in an object, to discover its constituent elements, to show their relationship, gives impetus to the construction of conceptual approaches. Visualization "dresses" schematic concepts in a bright, expressive artistic and graphic form. The handbook provides descriptions of the most popular means of analytical graphics - maps, graphs, tables, graphs, diagrams, flowcharts (algorithms), chronolents, maps, methodological schemes, etc. The ways

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Ruiz, Dana Catharine De. La Causa: The Migrant Farmworkers' Story. Raintree Steck-Vaughn, 1993.

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Murray, Myles N. William Murray, Esq.: Land agent in the Illinois Territory before the Revolutionary War. T. Gaus, 1987.

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Ghere, David L. Causes of the American Revolution: Focus on Boston : a unit of study for grades 7-12 / David L. Ghere, Jan F. Spreeman. Organization of American Historians : National Center for History in the Schools, 1998.

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Moneysmith, Marie. Grasas que engordan, grasas que curan: Conozca la diferencia entre las grasas que le hacen bien y las que le pueden causar enfermedades. Panorama Editorial, 2008.

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Sarmiento, Laura Judith Sandoval. Cuadernos tipográficos: Estudios y prácticas en torno a la tipografía en el suroccidente colombiano 2014-2016. Editorial Universidad del Cauca, 2017.

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Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in th

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Boller, David. Die letzten Tage der Menschheit: Eine Graphic Novel nach Karl Kraus. Edited by Pietsch Reinhard adapter editor and Kraus Karl 1874-1936. Herbert Utz Verlag GmbH, 2014.

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Chapitres de livres sur le sujet "Causal graphs"

Dayal, Vikram, and Anand Murugesan. "Causal Graphs." In Demystifying Causal Inference. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3905-3_5.

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Ma, Jing, Ruocheng Guo, and Jundong Li. "Causal Inference on Graphs." In Machine Learning for Causal Inference. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35051-1_4.

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Brumback, Babette A. "Causal Directed Acyclic Graphs." In Fundamentals of Causal Inference with R. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003146674-5.

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Foraita, Ronja, Jacob Spallek, and Hajo Zeeb. "Causal Directed Acyclic Graphs." In Handbook of Epidemiology. Springer New York, 2023. http://dx.doi.org/10.1007/978-1-4614-6625-3_65-1.

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Águeda, Cristina Puente. "Causal Relations, Text Mining and Causal Graphs." In Accuracy and Fuzziness. A Life in Science and Politics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18606-1_2.

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Jensen, Finn V. "Causal and Bayesian Networks." In Bayesian Networks and Decision Graphs. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3502-4_1.

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Gebharter, Alexander, and Marie I. Kaiser. "Causal Graphs and Biological Mechanisms." In Explanation in the Special Sciences. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7563-3_3.

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Pearl, J. "Statistics, Causality, and Graphs." In Causal Models and Intelligent Data Management. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58648-4_1.

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Mohan, Karthika. "Causal Graphs for Missing Data: A Gentle Introduction." In Probabilistic and Causal Inference. ACM, 2022. http://dx.doi.org/10.1145/3501714.3501750.

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Eherler, Dietrich, and Peter Kischka. "Decision Making Based on Causal Graphs." In Models, Methods and Decision Support for Management. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57603-4_18.

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Actes de conférences sur le sujet "Causal graphs"

Russo, Fabrizio, Anna Rapberger, and Francesca Toni. "Argumentative Causal Discovery." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/88.

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Causal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials. In this paper, we explore how reasoning with symbolic representations can support causal discovery. Specifically, we deploy assumption-based argumentation (ABA), a well-established and powerful knowledge representation formalism, in combination with causality theories, to learn graphs which reflect causal dependencies in the data. We prove that our meth

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Chen, Sirui, Mengying Xu, Kun Wang, et al. "CLEAR: Can Language Models Really Understand Causal Graphs?" In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.363.

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Mo, Zhaobin, Qingyuan Liu, Baohua Yan, Longxiang Zhang, and Xuan Di. "Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920273.

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Gholami, Mohammadreza, Mahshad Faramarzi, Nima Alipour, and Mansooreh Pakravan. "Node Embedding Extraction for Causal Brain Graphs in fMRI Data." In 2025 29th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2025. https://doi.org/10.1109/csicc65765.2025.10967434.

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Zhu, Jiageng, Kehao Li, Zheda Mai, Hanchen Xie, Wael AbdAlmageed, and Zubin Abraham. "Attention-Driven Causal Discovery: From Transformer Matrices to Granger Causal Graphs for Non-Stationary Time-series Data." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889219.

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Chou, Jingyuan, Jiangzhuo Chen, and Madhav Marathe. "Estimate Causal Effects of Entangled Treatment on Graphs using Disentangled Instrumental Variables." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825713.

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Tan, Fiona Anting, Jay Desai, and Srinivasan H. Sengamedu. "Enhancing Fact Verification with Causal Knowledge Graphs and Transformer-Based Retrieval for Deductive Reasoning." In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.fever-1.20.

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Sun, Wenjing. "Exploiting the Benefits of Popularity Bias Based on Causal Graphs in Optimizing Recommender Systems." In 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI). IEEE, 2024. http://dx.doi.org/10.1109/seai62072.2024.10674557.

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Chou, Jingyuan, Jiangzhuo Chen, and Madhav Marathe. "State-Of-The-Art and Challenges in Causal Inference on Graphs: Confounders and Interferences." In 2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 2024. https://doi.org/10.1109/cogmi62246.2024.00011.

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Tonon, Andrea, Meng Zhang, Bora Caglayan, et al. "RADICE: Causal Graph Based Root Cause Analysis for System Performance Diagnostic." In 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2025. https://doi.org/10.1109/saner64311.2025.00050.

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Rapports d'organisations sur le sujet "Causal graphs"

Turgeon, Mathieu. Causal Modeling with Regression Discontinuity Designs (RDD). Instats Inc., 2023. http://dx.doi.org/10.61700/s3nl5lfnmruqw469.

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This seminar introduces the use of the regression discontinuity design (RDD) to estimate treatment effects from observational data. Day 1 topics include directed acyclic graphs (DAG), potential outcomes framework and associated assumptions, the sharp regression discontinuity design, and tools for visualizing discontinuities. Day 2 topics focus on the estimation of values of interest from a regression discontinuity design, adopting a continuity-based approach to RD analysis. Day 2 will also cover issues related to the validation and falsification of the regression discontinuity design. An offic

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Turgeon, Mathieu. Causal Modeling with Regression Discontinuity Designs (RDD). Instats Inc., 2023. http://dx.doi.org/10.61700/o6e22r1sh4h7m469.

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This seminar introduces the use of the regression discontinuity design (RDD) to estimate treatment effects from observational data. Sessions 1-3 topics include directed acyclic graphs (DAG), potential outcomes framework and associated assumptions, the sharp regression discontinuity design, and tools for visualizing discontinuities. Sessions 4-6 topics focus on the estimation of values of interest from a regression discontinuity design, adopting a continuity-based approach to RD analysis. Day 2 will also cover issues related to the validation and falsification of the regression discontinuity de

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Naugle, Asmeret, Laura Swiler, Kiran Lakkaraju, Stephen Verzi, Christina Warrender, and Vicente Romero. Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1884926.

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Castleman, Benjamin, and Bridget Terry Long. Looking Beyond Enrollment: The Causal Effect of Need-Based Grants on College Access, Persistence, and Graduation. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19306.

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Lichter, Amnon, Joseph L. Smilanick, Dennis A. Margosan, and Susan Lurie. Ethanol for postharvest decay control of table grapes: application and mode of action. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7587217.bard.

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Original objectives: Dipping of table grapes in ethanol was determined to be an effective measure to control postharvest gray mold infection caused by Botrytis cinerea. Our objectives were to study the effects of ethanol on B.cinerea and table grapes and to conduct research that will facilitate the implementation of this treatment. Background: Botrytis cinerea is known as the major pathogen of table grapes in cold storage. To date, the only commercial technology to control it relied on sulfur dioxide (SO₂) implemented by either fumigation of storage facilities or from slow release generator pa

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Juden, Matthew, Tichaona Mapuwei, Till Tietz, et al. Process Outcome Integration with Theory (POInT): academic report. Centre for Excellence and Development Impact and Learning (CEDIL), 2023. http://dx.doi.org/10.51744/crpp5.

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This paper describes the development and testing of a novel approach to evaluating development interventions – the POInT approach. The authors used Bayesian causal modelling to integrate process and outcome data to generate insights about all aspects of the theory of change, including outcomes, mechanisms, mediators and moderators. They partnered with two teams who had evaluated or were evaluating complex development interventions: The UPAVAN team had evaluated a nutrition-sensitive agriculture intervention in Odisha, India, and the DIG team was in the process of evaluating a disability-inclus

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Reisch, Bruce, Avichai Perl, Julie Kikkert, Ruth Ben-Arie, and Rachel Gollop. Use of Anti-Fungal Gene Synergisms for Improved Foliar and Fruit Disease Tolerance in Transgenic Grapes. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7575292.bard.

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Original objectives . 1. Test anti-fungal gene products for activity against Uncinula necator, Aspergillus niger, Rhizopus stolonifer and Botrytis cinerea. 2. For Agrobacterium transformation, design appropriate vectors with gene combinations. 3. Use biolistic bombardment and Agrobacterium for transformation of important cultivars. 4. Characterize gene expression in transformants, as well as level of powdery mildew and Botrytis resistance in foliage of transformed plants. Background The production of new grape cultivars by conventional breeding is a complex and time-consuming process. Transfer

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Coelho, Danilo Santa Cruz. Nota Técnica n. 65 (Diest) : Inteligência artificial em justiça e segurança pública : exemplos e recomendações para políticas públicas. Instituto de Pesquisa Econômica Aplicada (Ipea), 2024. https://doi.org/10.38116/ntdiest65-port.

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Nesta nota, apresentamos quatro exemplos de pesquisas do Instituto de Pesquisa Econômica Aplicada (Ipea) nas áreas de justiça e segurança pública que utilizam algoritmos de IA: três no subcampo de Aprendizado de Máquina (Machine Learning) e um, ainda em andamento, no subcampo de Processamento de Linguagem Natural. Também destacamos duas experiências práticas nessas áreas nos EUA que evidenciam os graves danos que a aplicação irrefletida dessas ferramentas pode causar, especialmente a grupos minoritários e vulneráveis, servindo como alerta para que a sociedade evite replicar essas práticas sem

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Mbaye, Safiétou, Rémi Kouabenan, and Philippe Sarnin. L'explication naïve et la perception des risques comme des voies pour améliorer les pratiques de REX: des études dans l'industrie chimique et l'industrie nucléaire. Fondation pour une culture de sécurité industrielle, 2009. http://dx.doi.org/10.57071/311rex.

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L’analyse de l’accident soulève de nombreux enjeux dont le poids préfigure également des biais possibles dans l’explication causale et laisse apparaître que l’explication de l’accident peut difficilement être neutre. En l’occurrence, les difficultés rencontrées dans la conduite du REX relèvent en grande partie de conflits qui surviennent lors des analyses d’accidents. Ce document présente quatre études de terrain qui visaient à comprendre l’origine des conflits entre les acteurs du REX lors des analyses d’accidents et à mieux appréhender le rôle de la perception des risques et du climat de séc

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Homan, H. Jeffrey, Ron J. Johnson, James R. Thiele, and George M. Linz. European Starlings. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, 2017. http://dx.doi.org/10.32747/2017.7207737.ws.

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European starlings (Sturnus vulgaris, Figure 1)are an invasive species in the United States. The first recorded release of the birds was in 1890 in New York City’s Central Park. Because starlings easily adapt to a variety of habitats, nest sites and food sources, the birds spread quickly across the country. Today, there are about 150 million starlings in North America. Conflicts between people and starlings occur mostly in agricultural settings. Starlings damage apples, blueberries, cherries, figs, grapes, peaches, and strawberries. Starlings gather at concentrated animal feeding operations (C

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